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CLAUDE.md
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@ -36,28 +36,26 @@ Five Claude Code skills are installed in this repo's dev container. Each maps to
|-------|--------|-------------|
| **grill-me** | `/grill-me` | Before implementing — stress-tests a design through sequential questioning |
| **to-prd** | `/to-prd` | After a planning conversation — formalises context into a GitHub issue PRD |
| **ubiquitous-language** | `/ubiquitous-language` | When domain terms are drifting or ambiguous — builds/updates `UBIQUITOUS_LANGUAGE.md` |
| **grill-with-docs** | `/grill-with-docs` | When domain terms are drifting or new concepts are landing — challenges plans against `CONTEXT.md`, sharpens terminology inline, and writes ADRs for load-bearing decisions in `docs/adr/`. Replaces the older `ubiquitous-language` skill. |
| **tdd** | `/tdd` | During implementation — enforces vertical-slice TDD (one test → one impl → repeat) |
| **improve-codebase-architecture** | `/improve-codebase-architecture` | During refactoring — surfaces shallow modules and proposes deepening opportunities |
Domain glossary lives at [CONTEXT.md](./CONTEXT.md); load-bearing decisions live at [docs/adr/](./docs/adr/). The legacy [UBIQUITOUS_LANGUAGE.md](./UBIQUITOUS_LANGUAGE.md) is a redirect.
### Typical session chains
**Feature planning:**
`/grill-me` → `/to-prd` → `/ubiquitous-language`
`/grill-me` → `/to-prd` → `/grill-with-docs`
**Implementation:**
`/tdd` (+ `/grill-me` if a design fork appears mid-session)
**Refactoring:**
`/improve-codebase-architecture` → `/grill-me` → `/tdd` → `/ubiquitous-language`
`/improve-codebase-architecture` → `/grill-me` → `/tdd` → `/grill-with-docs`
### First time setting up?
New containers install all skills automatically via the Dockerfile. If you're in an existing container, run:
```bash
bash .devcontainer/backend/install-claude-skills.sh
```
Skills are installed automatically when the dev container is built, via the postCreate step that pulls from `Hestia-Homes/agentic-toolkit` (see `.devcontainer/backend/Dockerfile`). If an existing container is missing skills, rebuild the dev container.
## Type Safety

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# Ara
The Domna product for domestic retrofit modelling: ingests open-source EPC data, lets users correct or supersede it with their own surveys, and produces optimised retrofit packages for each property in a portfolio.
## Language
### Product
**Ara**:
The Domna product. Latin for "the altar"; named under Domna's classical-naming convention. Covers both the modelling product and the backend that powers it.
_Avoid_: ARA (acronym style), v2 backend, the new backend
**Domna**:
The company. Roman name; sibling to Ara in the same naming convention.
### Energy Performance Certificates
**EPC**:
An Energy Performance Certificate — a government-issued document rating a dwelling's energy efficiency from A (best) to G (worst).
_Avoid_: energy certificate, energy report
**Certificate Number**:
The unique identifier assigned to an EPC by the government registry.
_Avoid_: cert number, EPC ID
**Registration Date**:
The date an EPC was lodged with the government register; used to identify the most recent certificate for a property.
_Avoid_: assessment date, submission date
**EPC Band**:
A single letter A–G representing a property's current or potential energy efficiency rating.
_Avoid_: energy rating, EPC grade, EPC score
**Schema Type**:
The versioned RdSAP or SAP schema that describes the structure of an EPC's raw data (e.g. `RdSAP-Schema-21.0.1`).
_Avoid_: schema version, EPC format
**Domestic Certificate**:
An EPC issued for a residential dwelling, as opposed to a commercial one.
_Avoid_: residential EPC, home EPC
### Properties and addresses
**Property**:
The Ara domain aggregate representing a single dwelling under modelling: its identity, source data, enrichments, and modelling outputs.
_Avoid_: dwelling, unit, home, asset
**Properties**:
A first-class collection of Property objects; the unit of bulk operation in services.
_Avoid_: property list, batch (used for SQS chunks)
**UPRN**:
Unique Property Reference Number — the government-issued permanent identifier for a physical address in the UK.
_Avoid_: property ID, address ID, code
**Postcode**:
A UK postal code used to group nearby addresses; the primary search key for finding EPC records.
_Avoid_: zip code, postal code
**User Address**:
A free-text address string provided by a user or imported from a customer dataset, before any normalisation or matching.
_Avoid_: user input, raw address, user_inputed_address
**Comparable Properties**:
The reference cohort matched to a target Property by both geographic proximity (postcode prefix / UPRN range) and physical similarity (property type, built form, age band); used by the EPC Prediction Service for gap-filling and anomaly detection.
_Avoid_: neighbours, similar properties, peer set
### Source data
**Site Notes**:
The full-coverage record produced by a Domna survey of a single Property; carries every EPC field the modelling pipeline requires, and when present supersedes the public EPC for that Property — except when the public EPC is newer.
_Avoid_: energy assessment, site survey, field survey, Domna survey, Hestia survey
**Landlord Overrides**:
Property data supplied by a landlord that may correct or supplement the public EPC for a single Property; triggers Rebaselining when applied; not applicable when Site Notes are present.
_Avoid_: patches (deprecated), corrections, manual EPC, edits
### Modelling
**Effective EPC**:
The EpcPropertyData scored by the modelling pipeline for a single Property, derived from either Site Notes alone or the public EPC with Landlord Overrides applied; carries source-derived physical fields and originally recorded performance values, with model-rebaselined performance held separately in Baseline Performance.
_Avoid_: modelling EPC, working EPC, resolved EPC, derived EPC
**Rebaselining**:
Re-predicting a Property's SAP, carbon emissions, and heat demand via ML so the modelling pipeline scores it against the current SAP10 methodology. Triggered when either (a) the Effective EPC was lodged under a pre-SAP10 schema (`sap_version < 10.0`), so the recorded scores reflect a superseded methodology, or (b) Site Notes / Landlord Overrides changed the physical state of the Property (walls / heating / windows / etc.) so the lodged scores no longer reflect what's installed. Both triggers may fire together. Produces Effective Performance; Lodged Performance is preserved unchanged. Does not include kWh — that is always derived deterministically by EPC Energy Derivation.
_Avoid_: re-scoring, re-prediction, performance recomputation, refresh (for cache-freshness)
**Baseline Performance**:
A Property's current performance aggregate, holding both Lodged Performance and Effective Performance plus annual kWh / fuel split / bills derived from the Effective EPC. Persisted as one row; surfaced as one block in the UI.
_Avoid_: baseline predictions, predicted baseline, rebaselined values
**Lodged Performance**:
The SAP / EPC Band / carbon emissions / heat demand recorded on the public EPC (or the Site Notes' as-surveyed values when Site Notes are the source) — unmodified by modelling. The half of Baseline Performance that says "what the government register says about this Property".
_Avoid_: original performance, raw EPC values, recorded baseline
**Effective Performance**:
The SAP / EPC Band / carbon emissions / heat demand the modelling pipeline actually scored against — equal to Lodged Performance when no Rebaselining trigger fires, replaced by ML output when triggered. The half of Baseline Performance that says "what we modelled".
_Avoid_: modelled performance, rebaselined performance (only correct when rebaselining ran), scored values
**EPC Energy Derivation**:
The deterministic process that derives a Property's annual kWh, fuel split across heating, hot water, lighting, appliances and cooking, and bills from the Effective EPC — applying a UCL Correction for known EPC over/under-prediction and deducing fuel type from the SAP heating fields. No ML.
_Avoid_: kWh prediction, baseline kWh, energy estimation
**UCL Correction**:
The per-band linear correction (Few et al. 2023, _Energy & Buildings_ 288 113024) applied to EPC-modelled total primary energy use intensity to align it with metered consumption. Calibrated against gas-heated, non-PV homes in England and Wales rated under SAP 2012; the current implementation extrapolates it to all properties (open question §15.14).
_Avoid_: UCL adjustment, energy correction, metered correction
**EPC Anomaly Flag**:
A per-field indicator that a Property's value for an EPC field differs significantly from Comparable Properties; advisory only — surfaces in the UI to prompt user review, does not block modelling.
_Avoid_: outlier, mismatch, divergence flag
### Reference data
**Fuel Rates**:
The current per-fuel rate (pence/kWh) and standing charge used to compute a Property's bills; time-versioned and regional, refreshed from Ofgem's published caps via an ETL. The Smart Export Guarantee rate sits in the same set as `electricity_export`. Consumed by EPC Energy Derivation.
_Avoid_: fuel prices (commodity prices, different concept), tariff, energy cost
**Carbon Factors**:
The per-fuel CO2 emission factor (kgCO2e/kWh) used to compute a Property's carbon emissions; time-versioned, refreshed from Defra's annual publication. Consumed by EPC Energy Derivation.
_Avoid_: emission factors (ambiguous), CO2 rates
### Outputs
**Scenario**:
A named portfolio-level retrofit plan, built by a user in the scenario-builder UI and persisted before any modelling fires; carries the overall goal (e.g. Increasing EPC), budget, exclusions, housing type, and an ordered list of Scenario Phases. The model is triggered against one or more Scenarios at once; each Scenario yields one Plan per Property.
_Avoid_: project, batch, run-set
**Scenario Phase**:
One ordered step inside a Scenario, carrying a measure-type allowlist (e.g. "loft insulation and walls in phase 1; ASHP in phase 2"), an optional phase budget, and an optional phase target. A single-phase Scenario is one Scenario Phase with all measure types allowed and the full budget on it — there is no special-case path.
_Avoid_: scenario stage, scenario step, tranche
**Scenario Snapshot**:
A frozen copy of a Scenario pinned at trigger time, keyed by (task, scenario); used by the modelling pipeline so mid-run edits to the live Scenario do not affect an in-flight job. Snapshots are read-only and may be garbage-collected after the task completes.
_Avoid_: scenario version, frozen scenario, pinned scenario
**Plan**:
The per-Property output of one Scenario's modelling run; carries an ordered list of Plan Phases matching the Scenario's Phase shape. A Property modelled against N Scenarios in one trigger ends up with N Plans.
_Avoid_: recommendation set, output, result
**Plan Phase**:
The per-Property output of one Scenario Phase: the Optimised Package selected for that phase, the ending state snapshot (the Property's SAP / kWh / bills after the package is applied), and any Rolled-over Options that flow as candidates into the next Plan Phase.
_Avoid_: plan stage, plan step
**Rolled-over Options**:
Recommendations generated but not selected by the Optimiser in a given Plan Phase, that remain eligible as candidates in subsequent Plan Phases. Exact roll-over rule (automatic vs user-marked) is under design.
_Avoid_: deferred measures, leftover recommendations
**Recommendation**:
A single proposed retrofit measure for a Property, with its cost, SAP impact, kWh savings, carbon savings, and parts list.
_Avoid_: suggestion, option
**Optimised Package**:
The subset of a Property's Recommendations selected by the Optimiser Service for installation, chosen to satisfy the Scenario's goal subject to budget.
_Avoid_: selected measures, default measures, optimal solution, recommended bundle
**Measure Type**:
The catalogue classification of a retrofit measure (e.g. `solar_pv`, `loft_insulation`, `ashp`); one or more Recommendations reference the same Measure Type with property-specific cost and impact.
_Avoid_: measure (ambiguous), category
### Address matching
**Lexiscore**:
A similarity score in [0, 1] between a User Address and a candidate EPC address; combines token overlap and character-level similarity.
_Avoid_: score, match score, similarity
**Lexirank**:
Dense rank of candidates sorted by Lexiscore descending; rank 1 = best match.
_Avoid_: rank, position
**UPRN Candidate**:
An EPC Search Result that is a plausible match for a given User Address, before scoring decides the winner.
_Avoid_: match candidate, result
**Score Threshold**:
The minimum Lexiscore (currently 0.6) below which no match is returned even if a candidate exists.
_Avoid_: minimum score, cutoff
**Ambiguous Match**:
A matching outcome where two or more candidates share Lexirank 1, making it impossible to select a unique winner.
_Avoid_: tie, draw, duplicate
**Best Match**:
The single UPRN Candidate with Lexirank 1 that meets or exceeds the Score Threshold.
_Avoid_: winner, top result
### API and integration
**EPC Search Result**:
A lightweight record returned by the government domestic search endpoint — address lines, postcode, UPRN, band, and certificate number, but not full certificate data.
_Avoid_: search row, EPC row, result
**EPC Property Data**:
The fully mapped domain object produced after fetching and parsing a complete EPC certificate; the schema the modelling pipeline operates against.
_Avoid_: EPC data, certificate data, parsed EPC
**Old EPC API**:
The retired government API (`epc.opendatacommunities.org`) using HTTP Basic auth; decommissioned 30 May 2026.
_Avoid_: legacy API
**New EPC API**:
The replacement government API (`api.get-energy-performance-data.communities.gov.uk`) using Bearer Token auth.
_Avoid_: new API, current API
**Bearer Token**:
The auth credential required by the New EPC API; stored in the `EPC_AUTH_TOKEN` environment variable.
_Avoid_: API key, auth token, secret
## Relationships
- A **Property** represents a single physical dwelling for modelling; identified by `(portfolio_id, UPRN)` or `(portfolio_id, landlord_property_id)`.
- A **Property** has zero or more **EPCs** across time, exactly one **Effective EPC**, zero or one set of **Site Notes**, and zero or one set of **Landlord Overrides**.
- An **EPC** belongs to exactly one **Property** and has one **Certificate Number**.
- An **EPC** carries an **EPC Band** and is identifiable by its **Registration Date**; the most recent one is the current.
- A **UPRN** identifies a physical dwelling permanently; it does not change when the property changes owner — but each portfolio gets its own **Property** keyed against it.
- When a **Property** has both **Site Notes** and a public **EPC**, the newer of the two derives the **Effective EPC**. **Landlord Overrides** apply only when the **EPC** is the source — never when **Site Notes** are.
- A Property's **Baseline Performance** holds two halves: **Lodged Performance** (the gov register's SAP / band / carbon / heat) and **Effective Performance** (what the modelling pipeline scored against). The two are equal unless **Rebaselining** fires.
- **Rebaselining** produces **Effective Performance** by ML re-prediction when either (a) the Effective EPC was lodged under a pre-SAP10 schema, or (b) the Effective EPC's physical state diverges from the lodged EPC. **Lodged Performance** is never overwritten.
- **EPC Energy Derivation** contributes the annual kWh, fuel split, and bills on every Property unconditionally, reading current **Fuel Rates** and **Carbon Factors** from their respective repos.
- The **EPC Prediction Service** uses **Comparable Properties** for both gap-filling and producing **EPC Anomaly Flags**.
- A **Scenario** carries one or more ordered **Scenario Phases**. Triggering the model against N Scenarios produces N **Plans** per Property; each Plan carries an ordered list of **Plan Phases** matching the Scenario's shape.
- Each **Plan Phase** holds its **Optimised Package**, the ending state snapshot, and any **Rolled-over Options** that flow as candidates into the next Plan Phase. A single-phase Scenario is one Scenario Phase with all measure types allowed; the same machinery handles it.
- A **Scenario Snapshot** is pinned at trigger time per (task, scenario) so mid-run edits to the live Scenario do not affect an in-flight modelling job.
- A **Recommendation** references one **Measure Type** and carries property-specific cost and impact.
- **Address Matching** uses a **User Address** and **Postcode** to find a **UPRN** by scoring **UPRN Candidates** from an EPC search. A **Lexirank** of 1 with no **Ambiguous Match** and a **Lexiscore** ≥ the **Score Threshold** produces a **Best Match**.
## Example dialogue
> **Dev:** "A landlord uploads a corrected boiler for one of their properties. What happens?"
>
> **Domain expert:** "That's a **Landlord Override** on the heating fields. Save it against the **Property**. The **Effective EPC** has changed, so **Rebaselining** runs to re-predict SAP / carbon / heat, and **EPC Energy Derivation** re-runs to update kWh / bills based on the new fuel deduction. With fresh **Baseline Performance** we regenerate **Recommendations**."
> **Dev:** "What if the same Property also has Site Notes?"
>
> **Domain expert:** "**Site Notes** supersede the public **EPC**, so **Landlord Overrides** don't apply. We model from the **Site Notes** version of the **Effective EPC**. If the public **EPC** is newer than the **Site Notes**, that's the one exception — we use the newer one."
> **Dev:** "After modelling we end up with a list of measures. Which ones get installed?"
>
> **Domain expert:** "The **Optimiser Service** picks the **Optimised Package** — a subset of **Recommendations** that hits the **Scenario** goal within budget. The rest stay in the **Plan** as alternatives the user can swap in."
> **Dev:** "I'm looking at a property where the EPC says cavity walls but every other house on the street has solid. Is that a bug?"
>
> **Domain expert:** "That's an **EPC Anomaly Flag**. We compute it against the **Comparable Properties** for that postcode. It's advisory — the UI surfaces it and the landlord can apply a **Landlord Override** if it's wrong."
> **Dev:** "The property card shows two SAP scores side by side. Why?"
>
> **Domain expert:** "Those are **Lodged Performance** and **Effective Performance**. **Lodged** is what the gov register says — the EPC was rated under SAP 2012. **Effective** is what we scored against — we ran **Rebaselining** to predict the SAP10-equivalent rating because the methodology changed. Both stay on the **Baseline Performance** so users can see what's on record and what we're modelling against."
> **Dev:** "A landlord wants a 3-year retrofit plan — fabric work this year, heat pump next, solar after. How do we model that?"
>
> **Domain expert:** "Three **Scenario Phases** in one **Scenario**. Phase 1 allows fabric measures with this year's budget, phase 2 allows the heat pump with next year's budget, phase 3 allows solar. When we model, the **Optimiser Service** runs per phase against the rolling state — the heat pump is scored against the post-insulation property, not the original one. Each **Plan Phase** captures the **Optimised Package** plus the ending SAP / bills, and any **Rolled-over Options** that didn't make this phase's budget become candidates next phase."
## Flagged ambiguities
- **"property"** was historically warned against in favour of "dwelling"; that has been inverted. **Property** is now canonical for the Ara domain aggregate. Legacy code still uses "dwelling" in places — treat as alias.
- **"energy assessment"** in the existing codebase (`energy_assessment_functions`, `energy_assessments_by_uprn`) refers to what is now canonically called **Site Notes**. New code uses **Site Notes**.
- **"patch"** / `patch_epc` in the existing codebase has been merged into **Landlord Overrides**; the original concept is deprecated.
- **"already_installed measures"** in the existing codebase is likely subsumed by **Landlord Overrides** ("we have a heat pump now" → override the heating fields). Final call deferred to implementation.
- **"address"** appears as both the raw **User Address** (free-text) and a structured field on an **EPC Search Result** (normalised lines). Always qualify: "user address" vs "EPC address" or "address line 1".
- **"score"** is used for `AddressMatch.score()` output, the `lexiscore` column, and informally. Prefer **Lexiscore** in domain discussions; reserve "score" for method-level code comments.
- **"user_inputed_address"** in `backend/address2UPRN/main.py` is a misspelling and a synonym for **User Address** — the canonical term. New code should use `user_address`.
- **"EPC"** is overloaded as both the document and the rating band letter. Use **EPC** for the document, **EPC Band** for the letter.
- **"re-scoring"** has two meanings in the codebase — **Rebaselining** (re-predicting baseline performance after an EPC change) and post-optimisation measure re-prediction. Prefer **Rebaselining** for the former; for the latter, the **Optimiser Service** step does its own scoring without a special name.
- **"phase"** appears in two unrelated contexts: as cut-over timeline language in the PRD ("Phase 0 — Status quo", "Phase 1 — Forced cut-over") and as a domain concept in **Scenario Phase** / **Plan Phase**. Only the latter is a glossary term; cut-over phases are project-management vocabulary that does not enter code.
- **"stale"** appears in two senses: cache-freshness ("a Repo record is stale and the orchestrator should refetch") — a legitimate operational concept; and as loose shorthand for the EPC's recorded cost fields being unusable. The cost fields are not stale — they are pinned to the inspection-date fuel rates by design. Use "pinned to inspection date" or "pre-SAP10 schema" (whichever applies) instead.

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# Ubiquitous Language
Domain terminology glossary for this project. Generated and maintained by the `/ubiquitous-language` Claude Code skill.
This file has been **superseded by [CONTEXT.md](./CONTEXT.md)**.
Invoke `/ubiquitous-language` in any session to extract new terms from the conversation, flag ambiguities, and update this file with canonical definitions.
The project's domain glossary now lives at the repo root in `CONTEXT.md`, maintained by the `/grill-with-docs` skill (which replaced `/ubiquitous-language`).
---
## Energy Performance Certificates
| Term | Definition | Aliases to avoid |
|------|------------|------------------|
| **EPC** | An Energy Performance Certificate — a government-issued document rating a dwelling's energy efficiency from A (best) to G (worst). | "energy certificate", "energy report" |
| **Certificate Number** | The unique identifier assigned to an EPC by the government registry. | "cert number", "EPC ID" |
| **Registration Date** | The date an EPC was lodged with the government register; used to identify the most recent certificate for a property. | "assessment date", "submission date" |
| **EPC Band** | A single letter A–G representing a property's current or potential energy efficiency rating. | "energy rating", "EPC grade", "EPC score" |
| **Schema Type** | The versioned RdSAP or SAP schema that describes the structure of a certificate's raw data (e.g. `RdSAP-Schema-21.0.1`). | "schema version", "EPC format" |
| **Domestic Certificate** | An EPC issued for a residential dwelling, as opposed to a commercial one. | "residential EPC", "home EPC" |
## Properties and Addresses
| Term | Definition | Aliases to avoid |
|------|------------|------------------|
| **UPRN** | Unique Property Reference Number — the government-issued permanent identifier for a physical address in the UK. | "property ID", "address ID", "code" |
| **Postcode** | A UK postal code used to group nearby addresses; the primary search key for finding EPC records. | "zip code", "postal code" |
| **User Address** | A free-text address string provided by a user or imported from a customer dataset, before any normalisation or matching. | "user input", "raw address", "user_inputed_address" |
| **Dwelling** | A single residential unit that can hold an EPC — a house, flat, or maisonette. | "property", "unit", "home" |
## Address Matching
| Term | Definition | Aliases to avoid |
|------|------------|------------------|
| **Lexiscore** | A similarity score in [0, 1] between a user address and a candidate EPC address; combines token overlap and character-level similarity. | "score", "match score", "similarity" |
| **Lexirank** | Dense rank of candidates sorted by lexiscore descending; rank 1 = best match. | "rank", "position" |
| **UPRN Candidate** | An EPC search result that is a plausible match for a given user address, before scoring decides the winner. | "match candidate", "result" |
| **Score Threshold** | The minimum lexiscore (currently 0.6) below which no match is returned even if a candidate exists. | "minimum score", "cutoff" |
| **Ambiguous Match** | A matching outcome where two or more candidates share lexirank 1, making it impossible to select a unique winner. | "tie", "draw", "duplicate" |
| **Best Match** | The single UPRN candidate with lexirank 1 that meets or exceeds the score threshold. | "winner", "top result" |
## API and Integration
| Term | Definition | Aliases to avoid |
|------|------------|------------------|
| **EPC Search Result** | A lightweight record returned by the government domestic search endpoint — contains address lines, postcode, UPRN, band, and certificate number but not the full certificate data. | "search row", "EPC row", "result" |
| **EPC Property Data** | The fully mapped domain object produced after fetching and parsing a complete EPC certificate. | "EPC data", "certificate data", "parsed EPC" |
| **Old EPC API** | The retired government API (`epc.opendatacommunities.org`) using HTTP Basic auth; decommissioned May 2026. | "legacy API" |
| **New EPC API** | The replacement government API (`api.get-energy-performance-data.communities.gov.uk`) using Bearer token auth. | "new API", "current API" |
| **Bearer Token** | The auth credential required by the new EPC API; stored in the `EPC_AUTH_TOKEN` environment variable. | "API key", "auth token", "secret" |
## Relationships
- An **EPC** belongs to exactly one **Dwelling** and has one **Certificate Number**.
- A **Dwelling** may have multiple **EPCs** across time; the one with the most recent **Registration Date** is the current one.
- A **UPRN** identifies a **Dwelling** permanently; it does not change when the property changes owner.
- An **EPC Search Result** is a summary; it points to a full **EPC** via its **Certificate Number**.
- **Address Matching** uses a **User Address** and **Postcode** to find a **UPRN** by scoring **UPRN Candidates** from an EPC search.
- A **Lexirank** of 1 with no **Ambiguous Match** and a **Lexiscore** ≥ the **Score Threshold** produces a **Best Match**.
## Example dialogue
> **Dev:** "We have a user address and postcode. How do we find the UPRN?"
> **Domain expert:** "Search the **New EPC API** by **Postcode** — you get back a list of **EPC Search Results** for that area. Each one has an address and a **UPRN**. Score each against the **User Address** using the **Lexiscore**. If the top **UPRN Candidate** scores above the **Score Threshold** and there's no **Ambiguous Match**, that's your **Best Match**."
> **Dev:** "What if two results share the same address line 1?"
> **Domain expert:** "That's an **Ambiguous Match** — two candidates at **Lexirank** 1. Fall back to scoring on the full address using all address lines joined together. If that still ties, return nothing."
> **Dev:** "Once we have the best match, do we use the UPRN or fetch the full EPC?"
> **Domain expert:** "Depends on what you need. The **EPC Search Result** gives you the **EPC Band** and **Certificate Number**. If you need energy efficiency detail, use the **Certificate Number** to fetch the full **EPC Property Data**."
## Flagged ambiguities
- **"address"** appears as both the raw **User Address** (free-text from customer data) and a structured field on an **EPC Search Result** (normalised address lines). Always qualify: "user address" vs "EPC address" or "address line 1".
- **"score"** is used for the `AddressMatch.score()` function output, the `lexiscore` DataFrame column, and informally in conversation. Prefer **Lexiscore** in domain discussions; reserve "score" for method-level code comments.
- **"user_inputed_address"** in `backend/address2UPRN/main.py` is a misspelling and a synonym for **User Address** — the canonical term. New code should use `user_address`.
- **"EPC"** is overloaded as both the document (an Energy Performance Certificate) and the rating band letter. Use **EPC** for the document and **EPC Band** for the letter.
If you arrived here from a link in `CLAUDE.md` or older docs, follow the link above. This file is kept only to preserve git history and may be removed once internal references are updated.

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# ARA Backend Redesign — Design PRD
**Status**: Draft for team review
**Author**: Khalim Conn-Kowlessar (with Claude grill session)
**Branch**: `ara-backend-design-prd`
**Scope**: Service architecture + domain model + contracts for the new modelling backend. Linked sub-PRDs cover ML training pipeline, DB schema migration, and historical EPC re-mapping.
---
## 1. Context
### 1.1 The forcing function
The current modelling backend (`backend/engine/engine.py` — `model_engine`, 1331 LOC) was built as an MVP. It is:
- **Tightly coupled** to a specific gov EPC API that is being **decommissioned on 30 May 2026** (~17 days from today).
- **A monolith** — one async function reaches into DB modules, HTTP clients, ML lambdas, S3, and queue infrastructure directly.
- **Bottlenecked on a single person** — Khalim is the only contributor able to safely modify the engine because no one else can predict the blast radius of a change.
- **Already returning erroneous data** from the old API (clients are aware). The replacement API is partially built (`backend/epc_client/epc_client_service.py`) on the current feature branch.
### 1.2 What needs to change
Beyond just swapping API clients, this is the moment to **rebuild the backend into a production-grade, contribute-able codebase**, with:
- A clear domain model rooted in the new EPC schema (`EpcPropertyData`).
- Service boundaries that other team members can read, fix, and extend without needing the entire mental model.
- Repository-mediated persistence so business logic can be tested without spinning up a database.
- A separation between **data fetching** (slow, IO-heavy, external) and **modelling** (deterministic, fast, internal).
- Baseline kWh and bills derived deterministically from the Effective EPC (SAP physics + UCL correction + per-fuel rates from a refreshable repo) rather than from the EPC's recorded cost fields (which use fuel rates pinned to the inspection date) or from an ML kWh prediction.
### 1.3 Out of scope for this PRD
These ship as **linked sub-PRDs**:
- **Sub-PRD (ii) — ML training pipeline** (autogluon repo + parquet generation in this repo + scoring model retraining for the new EPC schema)
- **Sub-PRD (iii) — DB schema migration** (new tables: `site_notes`, `landlord_overrides`, EPC cache, parallel write strategy)
- **Sub-PRD (iv) — Historical EPC re-mapping** (one-off + ongoing batch job: legacy stored EPCs → new `EpcPropertyData` shape)
The contracts this PRD defines are the inputs each sub-PRD consumes.
---
## 2. Goals and non-goals
### 2.1 Goals
1. **Survive the 30 May API shutdown** — even if it means a brief degraded window, modelling continues to function against the new gov EPC API.
2. **Decouple data fetching from modelling** — modelling never makes external HTTP calls; it reads everything from repositories.
3. **Make every service unit-testable against fakes** — no test needs a real DB, a real gov API, or a real ML lambda to verify business logic.
4. **Establish a single `Property` aggregate root** as the domain centrepiece; all 9 modelling concerns are slices of one aggregate.
5. **Versioned ML data contract** — the EPC-to-features transform is the single shared artifact between this repo and the autogluon repo.
6. **Per-property UI surfaces** — fetched data can be shown to users for review and override **before** modelling runs; modelling is triggered separately. This will enable a landlord facing version of the product where we fetch the open data, present back to the user for review and then perform the modelling.
### 2.2 Non-goals
- Multi-region deploy, GDPR-class data minimisation work, or compliance reporting — separate workstreams.
- Replacement of the front-end. The new APIs preserve enough of the existing response shape that the FE migrates incrementally.
- Removing pandas. The ML transform output is a parquet-friendly DataFrame-like shape; that stays.
- A workflow engine (Prefect / Temporal / Airflow). Coordinator-class orchestration plus the existing SQS-fanout pattern is sufficient at the scale we serve.
---
## 3. Cutover plan
Forced cut-over, driven by the 30 May deadline. There is no strangler period because the Old EPC API death takes `model_engine` with it.
### 3.1 Phase 0 — Status quo (now → 30 May)
- `model_engine` keeps running against the Old EPC API for as long as it works.
- Build of the 9 new services starts **this week**, in parallel to the old engine continuing to serve traffic.
- The new `ara/` package lives alongside `backend/` but is not yet wired into any production endpoint.
- Goal: keep the lights on until the API dies; start the build immediately so the dark period is short.
### 3.2 Phase 1 — Forced cut-over (30 May onwards)
- On 30 May the Old EPC API dies; `model_engine` ceases to function for any new modelling run.
- Some downtime is expected and accepted. Clients are aware.
- Modelling resumes when the new pipeline is ready end-to-end. Remains to be decided if we have a per-portfolio flag, purely for the front end to reference old tables where necessary. No parallel pipelines, no traffic split — the new pipeline is the only pipeline.
- **Calico** and **Hyde** are the first live clients onto the new pipeline in June.
- `model_engine`, `SearchEpc`, the legacy `Property`, and surrounding modules in `backend/` are deleted once the new pipeline is serving all traffic.
### 3.3 What is *not* done
- No strangler — there is nothing to strangle once the Old EPC API dies on 30 May.
- No parallel-shadow run — would double compute and require diff tooling we don't have, while the old engine is already known to return bad data so diffs would be noise.
- TBC per-portfolio feature flag. Without this, the cut-over is all-or-nothing. All old portfolios are broken.
---
## 4. Architecture overview
```
┌─────────────────────────────────────────────────────────────────────┐
│ Trigger endpoint(s) │
│ (one or two — see §4.5; deferred decision) │
└───────────┬──────────────────────────────────────────┬──────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐
│ IngestionPipe │ SQS, batches of N │ ModellingPipe │
│ ----------- │ ◄─────────────────────│ ----------- │
│ Fetchers run │ │ Reads via Repos │
│ Persist via │ │ Calls Services │
│ Repos │ │ ML predictions │
└────────┬────────┘ └────────┬────────┘
│ │
└───────────────► Repos ◄─────────────────┘
│
▼
┌──────────────────┐
│ Postgres tables │
│ (property, │
│ epc_cache, │
│ site_notes, │
│ landlord_ │
│ overrides, │
│ plans, etc.) │
└──────────────────┘
┌──────────────────────────┐
│ RefreshOrchestrator │ triggers Ingestion → diff → conditionally Modelling
└──────────────────────────┘
```
### 4.1 Class taxonomy
Every class falls into exactly one of four roles:
| Role | Job | Examples |
|------|-----|----------|
| **Fetchers** | Call external APIs. Return raw response data. No DB. | `EpcClientService`, `GeospatialFetcher`, `SolarFetcher`, `SiteNotesIngester` |
| **Repos** | Persist and load domain aggregates. SQL hidden inside. No external IO. | `PropertyRepo`, `EpcCacheRepo`, `SiteNotesRepo`, `LandlordOverridesRepo`, `RecommendationsRepo`, `GenericDataRepo`, `SubtaskRepo` |
| **Services** | Business logic over domain objects. No external IO except via injected Fetchers / Repos. | `EpcRemappingService`, `EpcPredictionService`, `EpcEnergyDerivationService`, `KwhImpactService`, `ImpactPredictionService`, `RecommendationService`, `OptimiserService`, `FeatureBuilder`, `ResultsPersister` |
| **Orchestrators** | Compose Fetchers + Services + Repos to produce an end-to-end result. The only place where step order is encoded. | `IngestionPipeline`, `ModellingPipeline`, `RefreshOrchestrator` |
This taxonomy is **strict**. A class that fetches *and* persists belongs in the Service layer and depends on a Fetcher + a Repo. No back-channels.
### 4.2 Two pipelines, one direction
Data flows one way only: **Ingestion → Repos → Modelling**.
- **Ingestion** writes; never calls Modelling.
- **Modelling** reads; never calls Fetchers.
If Modelling needs fresh data, it returns "stale" and the caller decides whether to ingest first. This makes Modelling a pure function of repository state, which is the property that makes it reproducible, debuggable, and testable.
### 4.3 RefreshOrchestrator
Sits above both pipelines. Job:
1. Trigger `IngestionPipeline` for a portfolio.
2. After ingestion completes, ask repos: "did anything change vs the last modelled snapshot?"
3. If yes, trigger `ModellingPipeline`. If no, return early.
This avoids re-modelling 100k properties when only 200 had refreshed EPC data.
### 4.4 SQS fanout (preserved from current architecture)
The existing `trigger_plan_entrypoint` SQS-chunking pattern is kept. Both pipelines fan out per batch of ~30–100 properties (tuneable). Each consumer runs one batch end-to-end through the relevant pipeline.
UPRN partitioning: the trigger endpoint groups UPRNs by **locality** (postcode prefix / UPRN range) before chunking, so each batch maximises shared upstream fetches (one geospatial-range pull serves all 30 properties in the batch).
### 4.5 One endpoint for v1
For Phase 1 we ship **one trigger endpoint** that internally chains Ingestion → Modelling via `RefreshOrchestrator`. This matches the current FastAPI-fronted Lambda pattern (the FastAPI app in `services/<svc>/` is a thin entrypoint that invokes the modelling Lambda).
We can split into two endpoints later (refresh-only vs model-only) once a real workflow demands it — e.g. a Landlord-Override edit that should re-model without re-fetching open data. The class taxonomy and `RefreshOrchestrator` boundary allow this split without re-architecting.
### 4.6 Trigger contract
The trigger payload is reduced compared to today's `PlanTriggerRequest` ([backend/app/plan/schemas.py:98](../../backend/app/plan/schemas.py#L98)) — most of what's currently in the request body moves into the persisted `Scenario` aggregate.
```python
class ModelTriggerRequest(BaseModel):
portfolio_id: UUID
property_ids: list[UUID] | S3Ref # inline up to ~10k, S3 ref above
scenario_ids: list[UUID] # 1+; resolved + pinned to ScenarioSnapshot at fan-out
task_id: UUID
subtask_id: UUID # SQS state machine, preserved from today
```
Everything that used to ride at the top level dies or moves:
- `goal`, `budget`, `goal_value`, `inclusions`, `exclusions`, `required_measures`, `enforce_fabric_first`, `scenario_name`, `housing_type` → into `Scenario` / `ScenarioPhase`.
- `patches_file_path`, `already_installed_file_path`, `non_invasive_recommendations_file_path` → gone; Landlord Overrides covers all three.
- `valuation_file_path` → gone; `ValuationService` derives it.
- `ashp_cop`, `default_u_values` → `HeatingSystemAssumptionsRepo` / global config; not per-trigger.
- `multi_plan` → gone; `scenario_ids: list[...]` handles N runs natively (one Plan per scenario per property).
- `event_type`, `epc_certificate_number`, `lmk_key`, `file_format`, `sheet_name`, `index_start`/`index_end`, `file_type` → ingestion-side concerns; if needed, ride on a separate ingestion-trigger payload.
**Scenario snapshotting**: at fan-out time `RefreshOrchestrator` reads each requested `Scenario`, writes a `ScenarioSnapshot` keyed by `(task_id, scenario_id)`, and per-batch SQS messages reference the snapshot. Mid-run edits to the live `Scenario` do not affect an in-flight modelling job. Snapshots are read-only and can be garbage-collected after the task completes.
---
## 5. Domain model
### 5.1 Aggregate root: `Property`
`Property` is the centrepiece. Every service operates on one or more `Property` instances. Every repo writes one slice of `Property`. The aggregate carries all state for a single property's modelling run.
```python
@dataclass
class PropertyIdentity:
portfolio_id: UUID
uprn: Optional[int]
landlord_property_id: Optional[str]
address: AddressLines
postcode: str
@dataclass
class Property:
identity: PropertyIdentity
# --- Source data — modelling path is determined by which of these are set ---
epc: Optional[EpcPropertyData] # from gov API (or remapped historical)
site_notes: Optional[SiteNotes] # our own survey; supersedes EPC when present
landlord_overrides: Optional[LandlordOverrides] # sparse, only meaningful when epc set
# --- Enrichments ---
geospatial: Optional[GeoSpatial]
solar: Optional[SolarPotential]
epc_anomaly_flags: Optional[EpcAnomalyFlags] # from EpcPredictionService vs neighbours
# --- Modelling outputs ---
baseline_performance: Optional[BaselinePerformance] # carries lodged + effective pair; see §5.4
recommendations: list[Recommendation]
impact_predictions: Optional[ImpactPredictions]
plans: list[Plan] # one per Scenario the property was modelled against
# --- Derived ---
@property
def source_path(self) -> Literal["site_notes", "epc_with_overlay"]: ...
@property
def effective_epc(self) -> EpcPropertyData:
"""The EPC the modelling pipeline actually scores against."""
...
```
### 5.2 `Properties` collection
A first-class iterable, so batch operations are obvious:
```python
@dataclass
class Properties:
items: list[Property]
def __iter__(self) -> Iterator[Property]: ...
def __len__(self) -> int: ...
def filter(self, pred: Callable[[Property], bool]) -> "Properties": ...
def map(self, fn: Callable[[Property], Property]) -> "Properties": ...
def with_landlord_overrides(self) -> "Properties": ...
```
Services typically take and return `Properties`, not lists.
### 5.3 Other aggregates
| Aggregate | Owns | Repo |
|---|---|---|
| `Property` | property identity, epc, site_notes, landlord_overrides, enrichments, modelling results | `PropertyRepo` |
| `Plan` | per-property modelling output for one Scenario: ordered `phases: list[PlanPhase]`, each carrying its `OptimisedPackage`, ending state snapshot, and rolled-over options | `RecommendationsRepo` |
| `Scenario` | portfolio-wide scenario metadata (goal, budget, exclusions, housing type) plus ordered `phases: list[ScenarioPhase]`; each phase carries `measure_types_allowed`, phase budget, phase target | `RecommendationsRepo` |
| `ScenarioSnapshot` | frozen copy of a `Scenario` pinned at trigger time, keyed by `(task_id, scenario_id)`, so mid-run scenario edits don't affect an in-flight modelling job | `RecommendationsRepo` |
| `Subtask` / `Task` | SQS fanout state | `SubtaskRepo` |
| `EpcCache` | gov-API responses keyed by UPRN, with freshness/TTL | `EpcCacheRepo` |
| `GenericData` | UPRN-range geospatial, postcode lookups, shared static data | `GenericDataRepo` |
| `FuelRates` | time-versioned, region-aware per-fuel rates (pence/kWh), standing charges, SEG export rate, calorific values | `FuelRatesRepo` |
| `CarbonFactors` | time-versioned per-fuel CO2 emission factors (kgCO2e/kWh); Defra publishes annually | `CarbonFactorsRepo` |
| `HeatingSystemAssumptions` | boiler efficiency tables, ASHP/GSHP COPs, solar-thermal coverage proportion; per-property physical assumptions, not fuel-market data | `HeatingSystemAssumptionsRepo` |
Aggregates are loaded **whole** — never half a `Property`. If a slice is too large to load eagerly (e.g. recommendation history), it lives in a separate aggregate.
A single-phase Scenario is `phases: [<one ScenarioPhase>]` with all measure types allowed and the full budget on it — no special-case path through the pipeline.
### 5.4 `BaselinePerformance` carries lodged + effective
```python
@dataclass
class BaselinePerformance:
# As-lodged: unmodified EPC fields (or Site Notes' recorded values where Site Notes are the source).
lodged_sap: int
lodged_band: Epc
lodged_carbon: float
lodged_heat_demand: float
# Effective: what the modelling pipeline actually scored against.
# Equals lodged when neither rebaselining trigger fires; equals ML output when rebaselined.
effective_sap: int
effective_band: Epc
effective_carbon: float
effective_heat_demand: float
# kWh / fuel split / bills — always derived deterministically from the Effective EPC by
# EpcEnergyDerivationService (SAP physics + UCL correction + FuelRates lookup).
# Lodged kWh / bills are not stored separately — the EPC's recorded cost fields are pinned to
# inspection-date fuel rates, so we always re-derive bills from current FuelRates regardless.
annual_kwh: float
fuel_split: dict[Fuel, float]
annual_bills: dict[Fuel, float]
rebaselined: bool
rebaseline_reason: Optional[Literal["pre_sap10", "physical_state_changed", "both"]]
```
The pair lets the FE show "lodged rating vs SAP10-equivalent rebaselined rating" side by side without a separate query. Both fields are always populated; when no rebaselining trigger fires, `effective_*` equals `lodged_*`.
---
## 6. Source-of-truth and overlay precedence
There are exactly **two modelling paths**. The `Property.source_path` property selects.
### 6.1 Path 1 — Site notes
If a `Property` has `site_notes` and they are newer than any available EPC (or no EPC exists), site notes are the **complete** source of truth:
- `effective_epc` = `site_notes.to_epc_property_data()`.
- EPC fields not covered by site notes — **none expected**. Site notes are committed to being a full-coverage survey. Treat any gap as a survey-quality bug, not a fallback signal.
- `LandlordOverrides` are not applicable in Path 1 (the survey supersedes).
### 6.2 Path 2 — EPC with landlord overlay
If a `Property` has no site notes (or the EPC is newer):
- `effective_epc` = `epc` with `landlord_overrides` applied as a sparse field-level overlay (`landlord > epc`).
- `LandlordOverrides` are sparse: each row represents one corrected field. Schema TBD at implementation time; assume flat input via Excel/CSV for v1, with a flag to revisit shape after first customer onboarding.
### 6.3 Recency tie-break
When a property has **both** site notes and a public EPC, the newer of the two wins. Rationale: a recent EPC may reflect retrofit work done after our survey; conversely a recent survey reflects on-site observations the EPC cannot capture.
This tie-break is implemented in `Property.source_path` and may be tuned later (e.g. always prefer surveys regardless of date, or per-portfolio policy).
### 6.4 Rebaselining trigger
ML re-predicts SAP / carbon / heat when **either** of these holds:
1. **Pre-SAP10 schema** — `effective_epc.sap_version < 10.0`. The EPC was rated under SAP 2012 (or earlier) and we want a SAP10-equivalent baseline so all properties are scored against the same model version. Canonical signal is the `sap_version: float` field; fall back to `schema_type` string, then to `lodgement_date` if both are absent. Site Notes are assumed SAP10 by construction (PasHub / ECMK produce them now) — Path 1 typically doesn't trigger this leg.
2. **Physical state changed** — `effective_epc` differs from the lodged EPC's physical fields (walls / heating / windows / etc.). Triggered by Landlord Overrides changing physical state, or by Site Notes that contradict the lodged EPC.
When triggered, a single ML call re-predicts SAP/carbon/heat with the current Effective EPC state as input. Both reasons can fire together; the prediction is still one call.
kWh is **always** re-derived via `EpcEnergyDerivationService` — even when no ML rebaseline runs — because the EPC's recorded cost fields use fuel rates pinned to the inspection date, and current rates from `FuelRatesRepo` are what we want to surface to users.
The diff mechanism for "physical state changed" (content hash, dirty flag, etc.) is an implementation detail; start with a content hash of the physical-state subset of `EpcPropertyData` stored alongside the previous run.
### 6.5 Deprecated concepts
- **Patches** (`patch_epc`) — removed. Functionality subsumed by `LandlordOverrides`.
- **Already-installed measures** — likely subsumed by `LandlordOverrides` ("we have a heat pump now" → override heating fields). Confirmed at implementation time.
- **Non-invasive recommendations** — TBD whether this concept survives; not blocking.
---
## 7. Persistence: repositories and unit of work
### 7.1 What a repository is
A repository owns the SQL for one aggregate. Nothing else writes SQL for that aggregate. Callers see only domain objects.
```python
class PropertyRepo(Protocol):
def get(self, identity: PropertyIdentity) -> Optional[Property]: ...
def bulk_save(self, uow: UnitOfWork, properties: Properties) -> None: ...
def find_by_portfolio(self, portfolio_id: UUID) -> Properties: ...
def find_stale(self, portfolio_id: UUID, threshold: timedelta) -> Properties: ...
```
Implementation references current `db_funcs.*` modules during phase 0 to avoid a big-bang SQL rewrite, but the interface is fixed.
### 7.2 Unit of Work
Multi-table writes inside a single aggregate, or across aggregates that share a transaction (e.g. property + plan + recommendations) go through a `UnitOfWork`:
```python
with self.uow_factory() as uow:
self.property_repo.bulk_save(uow, properties)
self.recommendations_repo.bulk_save(uow, plans)
uow.commit()
```
UoW owns the SQLAlchemy session lifecycle. Repos use the session passed in via the UoW. Outside a UoW, repos use a short-lived read session.
### 7.3 Repository inventory
| Repo | Tables it owns |
|------|----------------|
| `PropertyRepo` | `properties`, `property_details_epc`, `property_spatial` |
| `EpcCacheRepo` | new table: `epc_api_cache` (TTL, raw API response, mapped `EpcPropertyData`) |
| `SiteNotesRepo` | new table: `site_notes` (replaces current `energy_assessments`) |
| `LandlordOverridesRepo` | new table: `landlord_overrides` (sparse, per-field rows for audit) |
| `RecommendationsRepo` | `plans`, `plan_phases`, `recommendations`, `recommendation_parts`, `scenarios`, `scenario_phases`, `scenario_snapshots` |
| `GenericDataRepo` | new table or S3-backed: UPRN-range geospatial + postcode-keyed shared static data |
| `FuelRatesRepo` | new table: `fuel_rates` — `(fuel_type, rate_pence_per_kwh, standing_charge_pence_per_day, calorific_value_kwh_per_unit, unit, effective_from, effective_to, region_code Optional, source)`. SEG export rate is a row with `fuel_type = 'electricity_export'`. |
| `CarbonFactorsRepo` | new table: `carbon_factors` — `(fuel_type, kgco2e_per_kwh, effective_from, effective_to, source)`. Defra publishes annually. |
| `HeatingSystemAssumptionsRepo` | new table(s): boiler efficiency, ASHP/GSHP COP, solar-thermal coverage proportion. Static-ish, manual refresh. |
| `SubtaskRepo` | `tasks`, `subtasks` (existing) |
DDL migrations are scoped to sub-PRD (iii).
### 7.4 Fakes
For tests, each repo has a `FakeXRepo` companion backed by a dict. Service unit tests inject fakes. No DB required.
---
## 8. ML contract
### 8.1 Where ML lives
| Concern | Owner |
|---|---|
| Defining the EPC → features transform | **This repo** (`ara.domain.ml.EpcMlTransform`) |
| Loading data, applying transform, writing training parquet to S3 | **This repo** (sub-PRD (ii) batch job) |
| Training, hyperparameter search, deployment | **Autogluon repo** |
| Scoring at modelling time | **This repo** (`FeatureBuilder` calls `EpcMlTransform`, sends DataFrame to deployed lambda) |
The autogluon repo is intentionally **dumb**: it consumes parquet, knows which column is the target, knows which columns to ignore. It has no EPC semantics.
### 8.2 `EpcMlTransform`
A separate class (not a method on `EpcPropertyData`), because:
- The data class stays clean of training-infrastructure concerns.
- Versioned transforms (`EpcMlTransformV1`, `EpcMlTransformV2`) swap easily.
- Future need: injection of normalisation stats from the training set is straightforward on a class, awkward on a dataclass.
```python
class EpcMlTransform:
VERSION: str = "1.0.0" # semver
def to_row(self, epc: EpcPropertyData) -> dict[str, Any]: ...
def to_rows(self, properties: Properties) -> pd.DataFrame: ...
def schema(self) -> dict[str, type]: ... # for parquet emission + validation
```
The interesting work — flattening `List[SapWindow]`, `List[SapBuildingPart]` into fixed-width columns — lives inside this class. Domain decisions (top-N windows, aggregate roofs, etc.) are encoded here and reviewed by Khalim. Sub-PRD (ii) goes into detail.
### 8.3 Versioning
- Transform class is **semver-tagged** (`VERSION = "1.0.0"`).
- S3 path for training parquet includes the version: `s3://.../training/v1.0.0/...`.
- Deployed scoring lambda is tagged with the transform version it was trained against.
- Modelling pipeline asserts at startup that its `EpcMlTransform.VERSION` matches the deployed lambda's tag; mismatch = hard fail at deploy time.
Bump major when removing or renaming columns. Bump minor when adding optional columns (older models still scoreable; new models can be trained against new fields).
### 8.4 ML model families
Both ML calls (rebaselining + per-measure impact) use the same `EpcMlTransform`:
| Service | Lambda | Target |
|---|---|---|
| `RebaseliningService` (S4b) | `baseline-models-*` | SAP / carbon / heat demand under the current Effective EPC state (SAP10-equivalent) |
| `ImpactPredictionService` (S6) | `impact-models-*` | SAP / carbon / heat demand impact per measure (and per battery option, using new EPC battery fields) |
Annual kWh and bills are never an ML target — derived deterministically by `EpcEnergyDerivationService` (S4a). Recommendation kWh delta is derived from the SAP delta predicted by S6 plus heating-system fuel + COP, not via a separate ML call.
The two families are trained against the same input feature schema; only target columns differ. Sub-PRD (ii) handles training-time details.
---
## 9. Service catalogue
The classes below implement the pipeline end-to-end. Detailed signatures are deliberately left for implementers — this PRD documents purpose, dependencies, and rough shape; per-service grill sessions produce the contracts.
**Out of the legacy engine** (deleted, not migrated): `PredictionMatrix` (debug-only, moves to test fixtures), `extract_portfolio_aggregation_data` (dead code, FE aggregates dynamically per §10), inspections plumbing (`inspections_map` is initialised but never populated in the current engine), patches / `already_installed` / `non_invasive_recommendations` (subsumed by Landlord Overrides), ECO4 / WHLG funding integration (`get_funding_data` and `optimise_with_scenarios`' funding paths), the pre-recommendation kWh ML lambda (`KWH_MODEL_PREFIXES`), and floor-count / heat-loss-perimeter estimation from geospatial (now on `EpcPropertyData`). Address matching (`address2UPRN`) lives as a separate service, not inside `EpcClientService`.
### 9.1 Fetchers (called by `IngestionPipeline`)
| # | Class | Purpose | Dependencies |
|---|---|---|---|
| F1 | `EpcClientService` | Fetches EPCs from new gov API. Already exists at `backend/epc_client/`. Scope narrows compared to current `SearchEpc` — address matching (`address2uprn`) and OS API estimation are not its concern. | httpx |
| F2 | `GeospatialFetcher` | Fetches UPRN-range geospatial data. Replaces `OpenUprnClient`. **Floor count and heat-loss perimeter estimation are no longer needed** — both are now on `EpcPropertyData` directly (`number_of_storeys`, `SapFloorDimension.heat_loss_perimeter_m`). Scope reduces to building geometry and postcode-area context. | S3 / Ordnance Survey API |
| F3 | `SolarFetcher` | Wraps Google Solar API; building-level + unit-level scenes. | Google Solar API |
| F4 | `SiteNotesIngester` | Loads site notes from Excel uploads / structured input. Persists via `SiteNotesRepo`. | S3, repo |
| F5 | `FuelRatesFetcher` | Scheduled ETL — scrapes Ofgem regional caps and per-fuel rates, writes timeseries rows to `FuelRatesRepo`. Manual CSV upload fallback for off-cycle corrections. | Ofgem feed, repo |
| F6 | `CarbonFactorsFetcher` | Same shape as F5 against Defra's annual CO2 factor publication. | Defra feed, repo |
### 9.2 Domain services (called by `ModellingPipeline`)
| # | Class | Original-list # | Purpose | Reads | Writes |
|---|---|---|---|---|---|
| S1 | `EpcRemappingService` | 4 | Re-map legacy / historical EPCs into new `EpcPropertyData` shape. | `EpcCacheRepo` | `EpcCacheRepo` (mapped column) |
| S2 | `EpcPredictionService` | 3 | For every property: produce predicted EPC + per-field anomaly flags vs neighbours. Used both for gap-fill (Path 2 if EPC missing) and UI surfacing. | `EpcCacheRepo`, `GenericDataRepo` | — |
| S3 | `FeatureBuilder` | (new) | Wraps `EpcMlTransform`. Converts `Properties` → scoring DataFrame. | — | — |
| S4a | `EpcEnergyDerivationService` | (new) | Derives annual kWh + fuel split + bills from the Effective EPC. Deterministic, no ML. Pipeline: (1) source regulated PEUI — either from `energy_consumption_current × floor_area` when EPC field present and no physical override, or from SAP physics (heat demand × area + SAP hot-water + SAP lighting) for Site Notes / overridden cases; (2) add appliance + cooking via SAP Appendix L formulas (port of [`AnnualBillSavings.estimate_appliances_energy_use`](../../backend/ml_models/AnnualBillSavings.py)); (3) apply UCL per-band correction (Few et al. 2023, Table 3), keyed on the **post-state Effective EPC's band** — not the lodged band; (4) decompose total PEUI into end-use shares via SAP-physics proportions; (5) primary→delivered per fuel using SAP primary factors; (6) bills = delivered kWh per fuel × current rate from `FuelRatesRepo` + standing charges + SEG credits. CO2 emissions from `CarbonFactorsRepo`. | `FuelRatesRepo`, `CarbonFactorsRepo`, `HeatingSystemAssumptionsRepo` | — |
| S4b | `RebaseliningService` | (new, partial overlap with old "rebaselining" logic) | Triggered by §6.4 conditions (pre-SAP10 schema **or** physical state changed). Calls SAP/carbon/heat ML lambdas to produce SAP10-equivalent baseline against the current Effective EPC state. Both `BaselinePerformance.lodged_*` and `effective_*` are populated downstream — pair is always stored, equal when not rebaselined. kWh is re-derived via S4a, not ML. | `FeatureBuilder` | — |
| S5 | `RecommendationService` | 6 | Generates per-property recommendations against the current rolling Effective EPC. Invoked **once per (scenario × phase)** — filters candidates to the phase's `measure_types_allowed`, returns candidates eligible against the post-prior-phase state. Replaces current `Recommendations` (1383 LOC). | `MaterialsRepo` | — |
| S6 | `ImpactPredictionService` | 7 | Calls SAP / carbon / heat impact ML lambda for **every** candidate recommendation (FE displays all options to user). Invoked per (scenario × phase) with the rolling state's feature vector. Recommendation kWh delta is derived deterministically from SAP delta + heating-system fuel/COP, not from a separate ML call. Battery impact uses the new EPC battery fields (`energy_pv_battery_count`, `energy_pv_battery_capacity`) as ML inputs — the deterministic `BatterySAPScorer` from the legacy engine is replaced by ML prediction. | `FeatureBuilder` | — |
| S7 | `OptimiserService` | 8 | Per-phase optimisation against rolling state. Reads `PlanPhase.state_at_end[n-1]` to honour cross-phase constraints (fabric-first, heat-pump-needs-insulation, ventilation). Wraps current `CostOptimiser` / `GainOptimiser` / `optimise_with_scenarios` minus the dead ECO-funding paths. Unselected candidates roll into phase n+1's candidate pool (auto vs user-marked TBD, §15). | — | — |
| S8 | `ValuationService` | — | Estimates per-property valuation (current + post-retrofit) from academic-paper-based regression on EPC change, property type, region. Improvement on the existing `PropertyValuation.estimate` code — exact shape deferred to per-service grill. | — | — |
| S9 | `ResultsPersister` | 9 | Final step: writes Plan (with `phases[]`) + Recommendations + Property updates via repos under one UoW, per scenario. | — | All write repos |
### 9.3 Orchestrators
| # | Class | Purpose |
|---|---|---|
| O1 | `IngestionPipeline` | Per-batch SQS consumer. Calls F1–F4, persists via repos. |
| O2 | `ModellingPipeline` | Per-batch SQS consumer. Reads from repos, runs S1→S8 in order, ends with persistence. |
| O3 | `RefreshOrchestrator` | Top-level: triggers Ingestion → diff → optionally Modelling. |
### 9.4 `ModellingPipeline` step order
For each `Property` in the batch, against each pinned `ScenarioSnapshot` from the trigger payload:
```
Per-property setup (runs once regardless of scenario count):
1. PropertyRepo.get() → Property (epc, site_notes, overrides, geospatial, solar)
2. EpcRemappingService — if epc is in legacy schema, upgrade to current
3. EpcPredictionService — predicted EPC + per-field anomaly flags (always runs)
4. Compute Property.effective_epc (path-1 or path-2)
5. RebaseliningService — IF §6.4 conditions hold (pre-SAP10 OR physical state changed),
re-predict SAP/carbon/heat via ML against the Effective EPC state.
Populate BaselinePerformance.lodged_* + effective_*.
6. EpcEnergyDerivationService — SAP-physics + UCL (post-state band) + FuelRates → kWh, fuel split, bills.
Per-scenario loop:
Per-phase loop (in scenario phase order):
7. RecommendationService — generate candidate measures, restricted to phase's measure_types_allowed,
against the rolling Effective EPC state (baseline for phase 1; updated for phase 2+).
8. ImpactPredictionService — predict SAP/carbon/heat impact for those candidates, ML scored against
the rolling state's feature vector. All candidates scored (FE shows options).
9. OptimiserService — select package within phase budget + phase goal. Reads earlier-phase state to honour
cross-phase constraints (fabric-first, heat-pump-needs-insulation, ventilation).
10. Apply package → roll state forward (simulate post-package SAP / kWh / bills via S4a + impact predictions
from step 8). Record `PlanPhase.state_at_end`. Unselected options become
`PlanPhase.rolled_over_options` and are eligible candidates next phase.
11. ResultsPersister — write Plan (phases[]) + Recommendations under one UoW for this scenario.
```
Steps 1–6 run **once per property** regardless of scenario count.
Steps 7–10 run **once per (scenario × phase)** per property.
Step 11 runs once per scenario per property.
Batching: steps 5, 8 batch the whole batch into one ML call where possible. Step 8's cost scales with `N_phases × N_scenarios × N_candidate_measures`; multi-phase pays its own ML bill, single-phase scenarios cost the same as today.
Note vs the current `model_engine`: the **pre-recommendation** kWh ML call has been removed. Baseline kWh now comes from `EpcEnergyDerivationService` (SAP physics + UCL + FuelRates). ML is reserved for SAP/carbon/heat (rebaselining + impact prediction). Recommendation-level kWh delta is derived deterministically from the impact-predicted SAP delta plus heating-system fuel + COP from `HeatingSystemAssumptionsRepo`; no separate kWh ML lambda.
**Open future change** (flagged §15): SAP-impact-of-a-measure is not strictly additive — installing measure A changes the SAP impact of measure B. The current per-measure ML scoring + linear optimisation approximates this. A future iteration may pre-define candidate packages and ML-score whole packages, accepting the combinatorial cost in return for accuracy. Defer until implementation reveals where the approximation hurts.
### 9.5 Per-service contracts — deferred
Method signatures, return types, error semantics, and edge-case behaviour are **explicitly out of scope** for this PRD. The implementer of each service runs a `/grill-me` session against this document and produces a detailed sub-design before coding.
---
## 10. Cross-batch concerns
| Concern | Status | Approach |
|---|---|---|
| Building-level solar adjustment | Deferred — future TODO, not implemented today. | The current `building_ids` block in `model_engine` is dead-ish; it operates on the in-process batch only. New design preserves that limitation. Future feature: a post-modelling consolidation pass that groups results by `building_id` across batches and re-optimises. |
| Portfolio aggregation | Dropped. | Front-end computes aggregations dynamically from per-property plans. `extract_portfolio_aggregation_data` in current engine is dead code (defined, never called) — deleting. |
| Shared upstream data | Handled by orchestrator partitioning + `GenericDataRepo`. | Trigger endpoint groups UPRNs by postcode / UPRN-range before SQS chunking so each batch maximises intra-batch sharing. `GenericDataRepo` caches across batches so first batch pays, subsequent batches hit cache. |
---
## 11. Repository layout — monorepo via uv workspaces
The repo is restructured as a Python monorepo using **uv workspaces**. Shared types and shared infra live as workspace packages under `packages/`; each deployable Lambda or microservice lives as its own package under `services/`. Each `services/<svc>/` has its own `pyproject.toml`, `Dockerfile`, and Lambda image — the bundle contains only that service's deps + its workspace deps, keeping cold-start size and package weight contained.
```
/
├── pyproject.toml # workspace root
├── uv.lock
│
├── packages/ # shared workspace packages — imported by services/
│ ├── domain/ # "domna-domain"
│ │ ├── pyproject.toml
│ │ └── src/domain/
│ │ ├── property.py # Property, Properties, PropertyIdentity
│ │ ├── site_notes.py
│ │ ├── landlord_overrides.py
│ │ ├── baseline_performance.py # lodged + effective pair
│ │ ├── plan.py # Plan, PlanPhase, OptimisedPackage
│ │ ├── scenario.py # Scenario, ScenarioPhase, ScenarioSnapshot
│ │ ├── recommendation.py
│ │ ├── geospatial.py
│ │ ├── solar.py
│ │ ├── anomaly_flags.py
│ │ └── ml/
│ │ ├── transform.py # EpcMlTransform (versioned)
│ │ └── schema.py
│ │
│ ├── repos/ # "domna-repos" — persistence, no business logic
│ │ ├── pyproject.toml
│ │ └── src/repos/
│ │ ├── unit_of_work.py
│ │ ├── property_repo.py
│ │ ├── epc_cache_repo.py
│ │ ├── site_notes_repo.py
│ │ ├── landlord_overrides_repo.py
│ │ ├── recommendations_repo.py
│ │ ├── generic_data_repo.py
│ │ ├── fuel_rates_repo.py
│ │ ├── carbon_factors_repo.py
│ │ ├── heating_system_assumptions_repo.py
│ │ └── subtask_repo.py
│ │
│ ├── fetchers/ # "domna-fetchers" — external API clients
│ │ ├── pyproject.toml
│ │ └── src/fetchers/
│ │ ├── epc_client.py # wraps backend/epc_client/
│ │ ├── geospatial.py
│ │ ├── solar.py
│ │ ├── fuel_rates_fetcher.py
│ │ └── carbon_factors_fetcher.py
│ │
│ └── utils/ # "domna-utils" — logging, AWS, S3, cloudwatch, subtasks
│ ├── pyproject.toml
│ └── src/utils/
│
├── services/ # deployable units, one Lambda image each
│ ├── ara/ # the modelling backend
│ │ ├── pyproject.toml # deps: domna-domain, domna-repos, domna-fetchers, domna-utils, ML libs
│ │ ├── Dockerfile
│ │ ├── src/ara/
│ │ │ ├── services/ # EpcRemappingService, EpcPredictionService,
│ │ │ │ # EpcEnergyDerivationService, RebaseliningService,
│ │ │ │ # FeatureBuilder, RecommendationService,
│ │ │ │ # ImpactPredictionService, OptimiserService,
│ │ │ │ # ValuationService, ResultsPersister
│ │ │ ├── orchestrators/ # IngestionPipeline, ModellingPipeline, RefreshOrchestrator
│ │ │ └── lambdas/ # handler.py per Lambda + event-shape contracts
│ │ └── tests/
│ │ ├── fakes/ # FakePropertyRepo, FakeEpcClient, etc.
│ │ ├── unit/ # service tests using fakes only
│ │ └── integration/ # real DB + real SQS via localstack
│ │
│ ├── address2uprn/ # messy-address → UPRN matching, pre-modelling step
│ │ ├── pyproject.toml
│ │ ├── Dockerfile
│ │ └── src/address2uprn/
│ ├── hubspot/ # existing Hubspot ETL
│ ├── pashub/ # PasHub survey ingestion
│ ├── ecmk/ # ECMK assessment ingestion
│ └── magicplan/ # MagicPlan integration
│
├── backend/ # legacy FastAPI app + microservices, kept until cut-over
│ ├── app/ # FastAPI; thin entrypoints that invoke service Lambdas
│ └── ... # legacy engine, SearchEpc, etc.; deleted after cut-over
│
├── datatypes/ # existing — EPC schemas; eventually folds into packages/domain/
└── docs/
└── adr/ # architectural decision records
```
**Boundary properties** (enforced by package structure, not convention):
- A `services/<svc>/` package can `import domain.*`, `import repos.*`, `import fetchers.*`, `import utils.*`. It **cannot** import another service's modules — they're separate distributions with no cross-import path.
- ADR-0003 (Ingestion / Modelling separation) is preserved: modelling services in `services/ara/src/ara/services/` depend only on `repos.*` + `domain.*`, never on fetchers. Orchestrators are the only place fetchers and services meet.
**Migration** (incremental, not big-bang):
1. Carve out `packages/domain/` first — fold `datatypes/epc/domain/` + the new aggregate types into it.
2. Carve out `packages/utils/` from current `utils/` + `backend/utils/`.
3. Carve out `packages/repos/` and `packages/fetchers/` once `services/ara/` is being built and needs them.
4. `services/ara/` is greenfield — no legacy code lives in it.
5. `services/address2uprn/`, `services/pashub/`, etc. are split out as their owners pick them up.
6. `backend/` shrinks to the FastAPI entrypoint layer once everything else has moved.
**Reused intact** (no rewrite needed at carve-out time):
- `backend/epc_client/` → folds into `packages/fetchers/src/fetchers/epc_client.py`.
- `datatypes/epc/domain/` → folds into `packages/domain/src/domain/epc/`.
- `recommendations/optimiser/` → wrapped by `services/ara/src/ara/services/optimiser.py`.
- `backend/app/db/` → repos delegate into `db_funcs.*` until SQL is rewritten under sub-PRD (iii).
---
## 12. Testing strategy
### 12.1 Unit tests (the bulk)
Every service test injects fake fetchers and fake repos. No DB, no network, no ML lambda. A service test verifies one slice of logic in 5–30 lines.
Example:
```python
def test_epc_prediction_flags_anomalous_wall_type():
neighbours = [_make_epc(wall_construction="solid") for _ in range(5)]
target = _make_property(epc=_make_epc(wall_construction="cavity"))
repo = FakeGenericDataRepo(neighbours_by_postcode={target.identity.postcode: neighbours})
svc = EpcPredictionService(generic_repo=repo)
result = svc.run(Properties([target]))
assert result[0].epc_anomaly_flags.wall_construction == "differs_from_neighbours"
```
### 12.2 Integration tests
One per pipeline (Ingestion, Modelling, Refresh). Real Postgres (testcontainers or localstack), fake fetchers (hitting recorded fixtures), fake ML lambdas (returning canned predictions). Catches schema / SQL / transaction issues.
### 12.3 Contract tests
The transform (`EpcMlTransform`) has its own test suite:
- Golden file: given a fixed `Property`, output matches an expected DataFrame row exactly.
- Schema test: the output columns exactly match a checked-in CSV header (so autogluon team sees breakage on PR).
### 12.4 What is NOT tested
- The autogluon repo's training code — owned there.
- The gov EPC API behaviour — assumed via the official spec.
- Front-end aggregation logic — owned there.
---
## 13. Observability
Each pipeline step emits a **structured log line** at start and end with:
```
{step, property_id, uprn, portfolio_id, subtask_id, duration_ms, outcome, error?}
```
Errors propagate with the `Property.identity` attached, so a portfolio of 100k can be triaged by grep.
The existing task/subtask state machine is preserved — `IngestionPipeline` and `ModellingPipeline` update subtask status at start (`in progress`), end (`complete` / `failed`), with the CloudWatch log URL attached as today.
CloudWatch alarms exist on subtask failure rate; thresholds remain unchanged.
---
## 14. Data flow: a worked example
A landlord uploads a corrected heating system for UPRN 12345 via the UI.
1. **UI** → `POST /properties/12345/overrides` → writes to `landlord_overrides` table via `LandlordOverridesRepo`.
2. **RefreshOrchestrator** invoked (either automatically on override-write, or by a "re-model" button). Notes: ingestion is *not* triggered because no external state changed.
3. **ModellingPipeline** invoked on a batch of `[12345]`:
- Reads `Property(uprn=12345)` from `PropertyRepo`.
- `Property.effective_epc` = epc + landlord_overrides → heating system fields differ from baseline.
- `RebaseliningService` triggered: ML re-predicts SAP / carbon / heat against the new effective EPC.
- `EpcEnergyDerivationService` re-runs over the new effective EPC to derive baseline kWh + fuel split + bills (no ML).
- `RecommendationService` regenerates recommendations against the new baseline.
- `OptimiserService` re-picks optimal package.
- `ResultsPersister` writes new plan under one UoW (old plan is superseded; whether to soft-archive is a sub-PRD (iii) decision).
Total external calls: zero. The override write is the only thing that hit a network boundary, and that was the inbound HTTP from the UI.
---
## 15. Open questions for team review
1. **One endpoint vs two** (§4.5) — **resolved**: single endpoint for Phase 1; split later when a real workflow demands it.
2. **`LandlordOverrides` shape** (§6.2) — flat-Excel-shape for v1, with a flag to revisit after first customer.
3. **`already_installed` and `non_invasive_recommendations`** (§6.5) — both likely subsumed by overlay, but final call deferred.
4. **Recency tie-break policy** (§6.3) — default "newer wins"; team to consider per-portfolio override.
5. **`GenericDataRepo` storage backend** — Postgres table, S3, or DynamoDB. Postgres is the path of least infra change; recommend defaulting to that.
6. **Soft-archive vs hard-overwrite** for superseded plans (§14) — affects audit / undo behaviour. Defer to sub-PRD (iii).
7. **Building-level optimisation as a Phase 2 service** (§10) — agreed deferred; flag for roadmap discussion.
8. **Transform versioning policy** (§8.3) — semver chosen; team to confirm bump conventions.
9. **UCL EPC-correction model** (§9.2 S4a) — **resolved**: Few et al. 2023 (Energy & Buildings 288, 113024). Implementation pattern already in [`AnnualBillSavings.adjust_energy_to_metered`](../../backend/ml_models/AnnualBillSavings.py) — port the per-band gradients/intercepts (Table 3) into `EpcEnergyDerivationService`, keyed on the post-state Effective EPC band.
10. **Fuel-price source for bill calculation** (§9.2 S4a) — **resolved**: `FuelRatesRepo` is a time-versioned, region-aware table; ETL by `FuelRatesFetcher` (Ofgem feed + manual upload fallback). Per-portfolio override deferred to v2 — confirm whether Calico / Hyde have bulk-buy contracts before first onboarding.
11. **kWh handling under Rebaselining** (§9.4) — **resolved**: ML re-predicts SAP/carbon/heat only; `EpcEnergyDerivationService` re-derives kWh from the rebaselined Effective EPC. Heating-fuel-type change is handled naturally because S4a re-reads heating fields from the Effective EPC.
12. **Phase rollover semantics** (§9.2 S7) — when a candidate measure isn't selected in phase n, does it auto-roll into phase n+1's candidate pool, or does the user mark which measure types can roll? Auto is simpler; user-marked is more flexible. Decide at scenario-builder UX time.
13. **Package-level vs per-measure ML scoring** (§9.4) — SAP impact of a measure is not strictly additive; the current per-measure scoring + linear optimisation approximates this. A future iteration may pre-define candidate packages and ML-score whole packages. Defer until per-service grill on `OptimiserService`.
14. **UCL extrapolation scope** (§9.2 S4a) — the Few et al. paper is gas-heated, no PV, England + Wales only. Current legacy code applies the correction to all properties regardless. Keep silent extrapolation for v1, or stratify (no correction for non-gas / PV) and surface uncertainty to FE? Defer to per-service grill.
15. **`ValuationService` rebuild** (§9.2 S8) — existing `PropertyValuation.estimate` cites several papers; the rebuild should improve the regression. Shape deferred to per-service grill.
16. **Battery-via-ML cutover** (§9.2 S6) — confirm the new ML model is trained against `energy_pv_battery_count` + `energy_pv_battery_capacity` and the legacy `BatterySAPScorer` can be retired without regression for battery-equipped properties.
---
## 16. Linked sub-PRDs (placeholders)
- **Sub-PRD (ii) — ML training pipeline** — `docs/sub-prds/ml-training-pipeline.md` (TBC)
- **Sub-PRD (iii) — DB schema migration** — `docs/sub-prds/db-schema-migration.md` (TBC)
- **Sub-PRD (iv) — Historical EPC re-mapping** — `docs/sub-prds/historical-epc-remap.md` (TBC)
Each sub-PRD owner: TBC. Each is independently reviewable but consumes the contracts defined in §5 (`Property` aggregate), §7 (repos), §8 (ML transform).
---
## 17. Next steps
1. Team review of this PRD (target: ~1 week).
2. Open follow-up grill sessions per service (`/grill-me` on each of S1–S8 + F1–F4) before that service is implemented.
3. Break into issues via `/to-issues` against the project tracker.
4. Stand up the empty `ara/` package skeleton + fakes + first integration-test scaffold as PR-1.
5. Land services in dependency order: domain → repos → fetchers → services → orchestrators → API.
Phase 1 milestone gate: first portfolio (Calico or Hyde) routed through the new pipeline end-to-end in June, with a manual spot-check on 5 representative properties to confirm outputs are reasonable. No parity-against-old-engine check — the old engine is dead by then.

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# Two source paths for a Property, not layered precedence
For modelling a Property we considered a strict layered precedence stack — `patches > site_notes > energy_assessment > epc > predicted` — with per-field provenance tracking. We rejected that in favour of **two strictly disjoint source paths**: a Property is modelled either from its Site Notes alone, or from the public EPC with Landlord Overrides applied on top. Site Notes are committed to being full-coverage by the domain ([CONTEXT.md](../../CONTEXT.md): _Site Notes_), so once we have them the EPC is irrelevant; conversely, Landlord Overrides are only meaningful when the EPC is the source of physical state.
The trade-off: layered precedence is more flexible (it tolerates a partial Site Notes survey by falling through to EPC for missing fields), but mixed-source data muddles the audit trail and undermines the "if we surveyed it, trust the survey" promise. The two-path model gives a cleaner derivation rule and an unambiguous source-of-truth per Property, at the cost of treating survey gaps as a survey-quality bug rather than a fallback signal. A Recency Tie-Break covers the one case where both exist: the newer of the two wins.
## Consequences
- Reversing this means rewriting `Property.effective_epc` and every service that reads it. Hard to roll back once 12 services depend on the two-path shape.
- Future addition of a third path (e.g. partial-survey) is a real change, not just a config tweak — flag it as an ADR if proposed.

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# `Property` is the aggregate root, not `EpcPropertyData`
The Ara modelling pipeline produces nine slices of per-property data (EPC, geospatial, solar, baseline performance, recommendations, optimised package, etc.). We considered making `EpcPropertyData` — the rich RdSAP-21-style EPC schema — the centrepiece, with other data hanging off it. We rejected that and introduced a new **`Property` aggregate root** that holds identity, all source data (EPC, Site Notes, Landlord Overrides), enrichments, and modelling outputs as named fields. Services take `Property` (or `Properties`) and return them with one slice populated.
Two reasons drove this:
1. **Geospatial, solar, recommendations, and overrides are peers to the EPC**, not properties of it. Putting them on `EpcPropertyData` conflates physical-state schema with modelling-run state.
2. **A typed `ModellingContext` dict-bag (the obvious alternative)** is exactly what the current legacy `Property` class became — 1259 lines of accumulated stuff, hard to read, hard to test, hard to extend. Named fields on a dataclass force the type system to keep us honest.
The cost is more domain types up front (`Property`, `Properties`, `PropertyIdentity`, `BaselinePerformance`, `OptimisedPackage`, etc.) and the discipline of one service writing one slice. The benefit is that every service has a single job and every test injects fake repos against a small, named structure.
## Consequences
- Every service signature accepts or returns `Property` / `Properties`. Refactoring later means touching all of them.
- `EpcPropertyData` stays a pure physical-state schema (defined in [datatypes/epc/domain/epc_property_data.py](../../datatypes/epc/domain/epc_property_data.py)) — no modelling outputs or run state on it.

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# Strict separation between Ingestion and Modelling
Data flows one way only: **Ingestion → Repos → Modelling**. Modelling services never make external HTTP calls; Ingestion services never run business logic. If Modelling needs fresh data, it sees a stale record in a repo and returns; the caller (a refresh orchestrator or the FE) decides whether to ingest first. We considered allowing modelling services to call fetchers directly on cache miss — convenient — and rejected it.
The trade-off is that modelling cannot "self-heal" by going to the gov EPC API when it finds stale data. The benefit is that modelling becomes a deterministic function of repository state: same Property in the repos, same modelling output. That is the property that makes modelling unit-testable against fakes (no DB, no network, no ML lambda), reproducible, and debuggable. It also enables a per-property UI flow where fetched data is shown to the user for review and possible override **before** modelling runs.
Under the rushed timeline this constraint is more valuable, not less. Mixing fetchers into services is the easy thing to do when shipping fast; once it's done it's hard to extract.
## Consequences
- Every modelling service depends only on Repos (and other Services / domain logic). No HTTP libraries in the modelling import graph.
- A `RefreshOrchestrator` is the only thing that calls Ingestion then Modelling in sequence; nothing else may.
- "Modelling is stale, refetch in-line" is a forbidden pattern — surface staleness, do not silently repair it.

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# Shared packages
Workspace packages consumed by `services/*`. Each package is its own Python distribution with its own `pyproject.toml`; services import via the workspace dependency mechanism (`{ workspace = true }`).
| Package | Purpose |
|---------|---------|
| [`domain/`](./domain/) | Shared domain types — `Property`, `BaselinePerformance`, `Plan`, `Scenario`, `EpcPropertyData`, etc. No persistence, no IO, no business logic. |
| [`repos/`](./repos/) | Persistence layer — one repo per aggregate. Owns the SQL. Depends on `domain`. |
| [`fetchers/`](./fetchers/) | External API clients (gov EPC, Ofgem, Google Solar, etc.). Depend on `domain` for response shapes. |
| [`utils/`](./utils/) | Cross-cutting infra — logging, S3, CloudWatch URL builders, SQS task helpers. |
## Adding a new shared package
Only when a real second consumer materialises. Don't pre-shatter (`repos-epc`, `repos-property`, ...) — split when a deployment needs to drop a dep, not before.
See [`../ara_backend_design.md`](../ara_backend_design.md) §11 for the broader monorepo layout and [`../CONTEXT.md`](../CONTEXT.md) for the domain glossary that names the types living in `domain/`.

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# domna-domain
Shared domain types — `Property`, `Properties`, `BaselinePerformance`, `Plan`, `PlanPhase`, `Scenario`, `ScenarioPhase`, `ScenarioSnapshot`, `Recommendation`, `OptimisedPackage`, `EpcPropertyData`, etc.
**Boundary**: types only. No persistence, no IO, no business logic. Other packages and services depend on `domna-domain`; this package depends on nothing internal.
Domain definitions live in [`../../CONTEXT.md`](../../CONTEXT.md). New types added here must match the glossary terms.
## Layout
```
src/domain/
├── __init__.py
├── property.py # Property, Properties, PropertyIdentity
├── site_notes.py
├── landlord_overrides.py
├── baseline_performance.py # lodged + effective pair (ADR-0004)
├── plan.py # Plan, PlanPhase, OptimisedPackage
├── scenario.py # Scenario, ScenarioPhase, ScenarioSnapshot (ADR-0005)
├── recommendation.py
├── geospatial.py
├── solar.py
├── anomaly_flags.py
└── ml/
├── __init__.py
├── transform.py # EpcMlTransform (versioned per §8.3)
└── schema.py
```
When `datatypes/epc/domain/` folds in, the EPC schema types move under `src/domain/epc/`.

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[project]
name = "domna-domain"
version = "0.1.0"
description = "Shared domain types for the Ara modelling pipeline and sibling Domna services."
requires-python = ">=3.11"
dependencies = []
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/domain"]

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"""Shared domain types for the Ara modelling pipeline and sibling Domna services.
No persistence, no IO, no business logic. See README.md for layout.
"""

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# domna-fetchers
External API clients. Each fetcher is responsible for one external system — `EpcClientService` for the gov EPC API, `GeospatialFetcher` for Ordnance Survey, `SolarFetcher` for Google Solar, `FuelRatesFetcher` for Ofgem, `CarbonFactorsFetcher` for Defra.
**Boundary**: makes HTTP calls + returns raw or lightly-mapped responses. No DB, no business logic. Modelling services never depend on fetchers — only orchestrators do (per [ADR-0003](../../docs/adr/0003-strict-ingestion-modelling-separation.md)).
## Layout
```
src/fetchers/
├── __init__.py
├── epc_client.py # wraps backend/epc_client/
├── geospatial.py
├── solar.py
├── fuel_rates_fetcher.py
└── carbon_factors_fetcher.py
```
`backend/epc_client/` will fold into `epc_client.py` during the migration; until then this module re-exports from the legacy location.

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[project]
name = "domna-fetchers"
version = "0.1.0"
description = "External API clients — gov EPC, Ofgem, Google Solar, Defra, etc."
requires-python = ">=3.11"
dependencies = [
"domna-domain",
"httpx>=0.27",
]
[tool.uv.sources]
domna-domain = { workspace = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/fetchers"]

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"""External API clients for Ara and sibling services.
One fetcher per external system. No DB, no business logic. See README.md.
"""

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# domna-repos
Persistence layer. One repo per aggregate; owns the SQL for its tables. Callers see only domain objects from `domna-domain`.
**Boundary**: depends on `domna-domain` for types. No external IO except the DB. No business logic — services do that.
## Repos (per [PRD §7.3](../../ara_backend_design.md))
```
src/repos/
├── __init__.py
├── unit_of_work.py
├── property_repo.py
├── epc_cache_repo.py
├── site_notes_repo.py
├── landlord_overrides_repo.py
├── recommendations_repo.py
├── generic_data_repo.py
├── fuel_rates_repo.py
├── carbon_factors_repo.py
├── heating_system_assumptions_repo.py
└── subtask_repo.py
```
Each repo has a `Fake*Repo` companion in its service's test tree (typically `services/ara/tests/fakes/`) — dict-backed, no DB.
DDL migrations are scoped to sub-PRD (iii); during Phase 0 repos may delegate into the legacy `backend/app/db/db_funcs.*` modules.

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[project]
name = "domna-repos"
version = "0.1.0"
description = "Persistence layer — one repo per aggregate. Owns the SQL."
requires-python = ">=3.11"
dependencies = [
"domna-domain",
"sqlalchemy>=2.0",
]
[tool.uv.sources]
domna-domain = { workspace = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/repos"]

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"""Persistence layer for the Ara domain aggregates.
One repo per aggregate. Owns SQL; exposes domain objects. See README.md.
"""

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# domna-utils
Cross-cutting infrastructure helpers. Nothing domain-specific — anything in here should be portable across services.
## Will live here (migrating from `utils/` and `backend/utils/`)
- Logging — `logger.py`
- S3 — `s3.py`
- Pandas helpers — `pandas_utils.py`
- CloudWatch URL builder — `cloudwatch.py`
- SQS subtask helpers — `subtasks.py`
## Will NOT live here
Service-specific parsers (Osmosis condition report, full-SAP parser, SharePoint integration) move into the service that owns them, not here.

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[project]
name = "domna-utils"
version = "0.1.0"
description = "Cross-cutting infrastructure helpers — logging, S3, CloudWatch, SQS tasks."
requires-python = ">=3.11"
dependencies = [
"boto3>=1.34",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/utils"]

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"""Cross-cutting infrastructure helpers — logging, S3, CloudWatch, SQS tasks.
Nothing domain-specific belongs here. See README.md.
"""

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[tool.pyright]
# uv workspace root.
# Each workspace member has its own pyproject.toml under packages/<name>/ or services/<name>/.
# Run `uv sync` at the root to install everything; `uv sync --package <name>` for one.
[tool.uv.workspace]
members = [
"packages/domain",
"packages/repos",
"packages/fetchers",
"packages/utils",
"services/ara",
]

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# Services
Each subdirectory is a deployable unit — typically a Lambda image. Own `pyproject.toml`, own `Dockerfile`, own deps. Lambda bundle contains only that service's deps + its workspace deps.
| Service | Purpose |
|---------|---------|
| [`ara/`](./ara/) | The Domna retrofit modelling backend — ingestion + modelling pipelines, all 9 services in [PRD §9.2](../ara_backend_design.md). |
Other Domna services (address2uprn, hubspot, pashub, ecmk, magicplan) live in the legacy `backend/` and `etl/` trees for now; they are slated to migrate here as their owners pick them up — see [PRD §11](../ara_backend_design.md). When that work starts, scaffold the service under `services/<name>/` and add it to the workspace members in the root `pyproject.toml`.
## Service boundary
A service can `import domain.*`, `import repos.*`, `import fetchers.*`, `import utils.*` (workspace deps). It **cannot** import another service's modules — they are separate distributions with no cross-import path. This is the structural enforcement of the modelling/ingestion separation ([ADR-0003](../docs/adr/0003-strict-ingestion-modelling-separation.md)).

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# Lambda image for the Ara modelling backend.
#
# This is a scaffold — final image will install only ara + its workspace deps
# (domna-domain, domna-repos, domna-fetchers, domna-utils) plus ML/data libraries.
# Build via uv to keep cold-start size contained.
FROM public.ecr.aws/lambda/python:3.11
# TODO: install uv, sync this service's deps from the workspace lock file,
# copy src/ara/ into ${LAMBDA_TASK_ROOT}/, set CMD to the Lambda handler.
CMD ["ara.lambdas.handler.handler"]

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# ara
The Domna retrofit modelling backend. Replaces the legacy `backend/engine/engine.py` monolith with a service-oriented pipeline that survives the 30 May 2026 gov EPC API cut-over and that other team members can read, fix, and extend.
Design document: [`../../ara_backend_design.md`](../../ara_backend_design.md).
Domain glossary: [`../../CONTEXT.md`](../../CONTEXT.md).
## Layout
```
src/ara/
├── services/ # the 9 domain services from PRD §9.2:
│ # EpcRemappingService, EpcPredictionService,
│ # FeatureBuilder, EpcEnergyDerivationService,
│ # RebaseliningService, RecommendationService,
│ # ImpactPredictionService, OptimiserService,
│ # ValuationService, ResultsPersister
├── orchestrators/ # IngestionPipeline, ModellingPipeline, RefreshOrchestrator
└── lambdas/ # one handler.py per Lambda + the event-shape contracts
```
## Pipeline
See [PRD §9.4](../../ara_backend_design.md) for the per-batch step order. Briefly: per-property setup (steps 1–6) runs once per Property; the per-scenario × per-phase loop (steps 7–10) re-derives candidates and impact predictions against the rolling Effective EPC state; results are persisted under one Unit of Work per (Plan, Scenario).
## Testing
- `tests/unit/` — service tests against fakes from `tests/fakes/`. No DB, no network, no ML lambda.
- `tests/integration/` — real Postgres (testcontainers / localstack), fake fetchers + fake ML lambdas.
- ML transform contract tests live with `domain.ml.transform` in `packages/domain/`.

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[project]
name = "ara"
version = "0.1.0"
description = "The Domna retrofit modelling backend. Ingestion + modelling pipelines."
requires-python = ">=3.11"
dependencies = [
"domna-domain",
"domna-repos",
"domna-fetchers",
"domna-utils",
"pandas>=2.0",
"pandas-stubs",
"numpy>=1.26",
"pydantic>=2.0",
]
[tool.uv.sources]
domna-domain = { workspace = true }
domna-repos = { workspace = true }
domna-fetchers = { workspace = true }
domna-utils = { workspace = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/ara"]

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"""The Domna retrofit modelling backend.
See README.md and ara_backend_design.md (repo root) for the architecture.
"""

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"""Lambda handlers + event-shape contracts.
One handler per deployable Lambda. See PRD §4.6 for the ModelTriggerRequest
shape.
"""

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"""Orchestrators for the Ara pipeline.
IngestionPipeline, ModellingPipeline, RefreshOrchestrator. The only place
where step order is encoded and where fetchers + services + repos meet.
"""

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"""Domain services for the Ara modelling pipeline (PRD §9.2).
EpcRemappingService, EpcPredictionService, FeatureBuilder,
EpcEnergyDerivationService, RebaseliningService, RecommendationService,
ImpactPredictionService, OptimiserService, ValuationService, ResultsPersister.
Each service operates on `Properties` and depends only on repos + other services
+ domain objects. No external IO (per ADR-0003).
"""

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"""Fake repos and fetchers for unit tests.
One Fake<Name>Repo per real repo; dict-backed; no DB. Same for fetchers.
"""

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