Retrieval-augmented knowledge systems.
RAG pipelines from ingestion through retrieval, ranking, eval and observability. Grounded answers, controllable behaviour, costs that make sense — at the scale of your real document corpus.
The problem we solve
RAG looks like five lines of LangChain in a tutorial. In production it's an engineering discipline: ingesting messy documents reliably, chunking sensibly, retrieving with the right hybrid strategy, ranking properly, evaluating against a real dataset, and watching it drift as your corpus grows. We've shipped enough of these to know which decisions matter and which are noise.
What we ship
- 01Document ingestion pipelines for PDFs, HTML, Notion, Confluence, S3, etc.
- 02Chunking strategies tuned for your content shape
- 03Embeddings model selection on your eval set, not benchmarks
- 04Hybrid retrieval: vector + lexical + metadata filters
- 05Re-ranking with cross-encoders or LLM rerankers
- 06Retrieval evaluation: nDCG, recall@k, against a labelled set
- 07Grounded generation with citations and confidence
- 08Per-tenant isolation in multi-tenant RAG systems
- 09Incremental indexing as your corpus changes
- 10Cost and latency dashboards across the pipeline
What you receive
- Production RAG system with documented invariants
- Eval dataset for retrieval and generation quality
- Re-indexing and re-evaluation runbook
- Observability dashboards for retrieval and generation
Stack we reach for
Ideal for
- → Companies drowning in unstructured documents users need to query
- → Customer support teams who want answers grounded in product docs
- → Internal knowledge tools where general LLMs hallucinate badly
- → Legal, medical, financial domains demanding citations and provenance
How an engagement runs
- 01
Eval first
We build an evaluation set from real queries and real expected answers before touching the model. Without it, every change is opinion.
- 02
Ingestion & retrieval
Document pipeline, chunking and retrieval tuned against the eval. Hybrid strategies tested, not assumed.
- 03
Generation
Grounded generation with citations, structured output where appropriate, fallback behaviour for low-confidence cases.
- 04
Operate
Observability, drift monitoring, re-indexing automation, per-query cost tracking.
How to engage
RAG Feasibility
Document audit, eval set construction, prototype on your real corpus.
RAG Build
End-to-end RAG system production-ready, with evals and operational maturity.
RAG Operate
Continuous tuning as your corpus and use cases evolve.
Frequently asked.
01Why not just fine-tune?
Fine-tuning is rarely the right answer for fact retrieval — it makes models know about your data, not look it up. RAG keeps citations possible, updates trivial and costs sane. We use fine-tuning where style or domain language matters.
02Which vector database?
Postgres + pgvector unless your scale or feature set forces something more specialized. Most teams never need a dedicated vector DB and pay heavily for the complexity.
03How do you measure quality?
A labelled eval set built from real user queries. Retrieval metrics (recall, nDCG), answer quality with LLM-as-judge for scale plus human review on a sample.
Have a problem worth solving well?
Tell us the outcome you want. We'll tell you what it takes — honestly, within a week, in writing.
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