How it works
Five stages. One operating model.
We don't reinvent the wheel for every engagement. This is the pipeline we run, refined across dozens of enterprise deployments.
01 · Assess
Two-week diagnostic across your sources, schemas, and current retrieval quality.
Inputs- Data source inventory
- Sample queries and gold answers
- Stakeholder interviews
Outputs- Gap analysis
- Retrieval baseline
- Prioritized roadmap
Tools- Custom eval harness
- Lineage discovery
- Schema profilers
02 · Clean
Dedup, normalize, redact PII, and resolve entities across systems of record.
Inputs- Tabular sources
- Unstructured corpora
- Access policies
Outputs- Cleaned datasets
- Entity-resolved keys
- PII-redacted text
Tools- DBT
- Presidio
- Custom resolvers
03 · Build Pipeline
Ingestion, chunking, embedding, indexing — orchestrated and version-controlled.
Inputs- Cleaned corpora
- Domain ontology
- Retrieval requirements
Outputs- Versioned indexes
- Reproducible pipeline
- Observability hooks
Tools- Pinecone / Weaviate
- Airflow / Dagster
- OpenAI / Anthropic / OSS embeddings
04 · Evaluate
Golden datasets, retrieval metrics, hallucination scoring. Ship with evidence.
Inputs- Gold Q&A pairs
- Subject-matter reviewers
- Production traces
Outputs- Quality dashboard
- Regression gates
- Model + prompt selection
Tools- RAGAS
- LangSmith
- Custom evals
05 · Operate
Monitoring, drift detection, scheduled re-indexing, and incident response.
Inputs- Production telemetry
- User feedback
- Source updates
Outputs- Uptime SLAs
- Drift alerts
- Quarterly reviews
Tools- Prometheus / Grafana
- Datadog
- PagerDuty
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