# mql-enrichment-service

Internal MQL enrichment platform at {{cutting_edge_saas_company}}, owned by RevOps-AI. Reads inbound paid-acquisition leads, enriches with firmographic and intent signals, routes to the SDR queue.

This is the Claude Code context file. The full operating model is in `OPERATING-MODEL.md`; that is the first doc you read.

## What this is and is not

- **Is:** an `internal-platform` AI-shaped function. Three L5 platforms under one L0 strategy. Mature across all eight layers per the latest audit.
- **Is not:** a customer-facing product (no L1 brand surface). Not a model trainer. Not a data warehouse (it consumes from `revops.mql_facts`; does not own that table).

## Resolution order

When working in this instance:

1. `BOOTSTRAP-BRIEF.md` (in the kit) - the operating standard
2. `OPERATING-MODEL.md` - this instance's summary and current maturity
3. `policies/` - L0 (strategy), L1 (N/A-explicit), L2 (philosophy), L3 (framework contract)
4. `kit/` - L4 platform kit (mirrored from `{{platform_team_name}}/ai-kit`)
5. `platforms/{{platform}}/dossier.md` - L5 instance dossiers
6. `cross-platform/` - L7 glue artifacts

## Rules specific to this instance

- **No competitor product references in any output, ever.** Per L2 red line. Output guardrail enforces; trips alert {{trust_team}}.
- **Consent check before any enrichment.** Per ADR-0003 and the consent-flow artifact. Cache TTL 5 minutes; revocation propagates within that window.
- **No silent overwrites of SDR manual edits.** Per L2 red line. SDR edits are authoritative; the enrichment service annotates rather than overwrites.
- **Reproducibility quadruple on every call.** Per L3. Missing any of the four quadruple fields fails CI.
- **Mirror discipline.** Kit components carry `MIRRORED FROM` headers. We do not edit mirrored files locally; we open an inbox entry to `{{platform_team_name}}` and propose the change upstream.

## Tooling stack

- Cloud: AWS (us-east-1 primary, eu-west-1 for EU tenants)
- Observability: {{observability_platform}} (board id `revops-mql-pipeline-001`)
- Warehouse: {{warehouse_name}} (`revops.mql_facts` is the canonical fact table)
- Identity: {{idp_name}} (tenant-scoped JWT)
- Ticketing: Linear (project `RAI`)
- Comms: Slack (`#revops-ai`, `#revops-ai-alerts`)
- Long-form docs: Confluence (`RevOps AI` space)

## Decision framework

The five questions from BOOTSTRAP-BRIEF Section XIV apply unchanged here. Specifically:

1. Does this serve the customer (SDRs and the downstream revenue motion)?
2. Does this respect privacy (consent-flow, PII tenant locality)?
3. Is this the simplest approach that works (avoid model-bumping when prompt or data fix would suffice)?
4. Could a capable replacement operate this from the artifacts alone?
5. Does the fully-loaded cost (compute + review + Compliance attention) stay proportional to the value (>= 42% SDR-accepted-MQL rate)?

## Cross-references

- `OPERATING-MODEL.md` - read first if new here
- `MATURITY-SCORECARD.md` - current audit
- `BACKLOG.md` - pending work
- `HANDOFF.md` - custody state
