Your knowledge base, answerable in seconds.
For teams with institutional knowledge locked in docs, tickets, wikis, and databases. We build RAG pipelines that answer accurately, cite sources, know when to say 'I don't know,' and hold up at 3am.
We connect an AI model to your own documents so it answers from your policies, prices and manuals rather than from whatever it absorbed in training. Answers cite the source. When the data does not cover a question, it says so instead of guessing.
Updated August 2026
Three ways to make AI know your business.
Fine-tuning gets suggested most and is usually the wrong first move.
Most RAG systems fail not because of the LLM — but because of the retrieval.
The failure pattern is always the same: someone chunks documents naively, throws them in a vector store, and wonders why the system hallucinates or misses obvious answers. The LLM is fine. The retrieval is broken. We start with the retrieval.
We design retrieval pipelines with hybrid search (BM25 + vector), query rewriting, and re-ranking — because any single retrieval strategy has blind spots. We then build the evaluation harness before we write prompts: ground-truth question-answer pairs that tell us, objectively, whether the system is getting better or worse as we change it.
We also build the operational tooling: index drift monitoring, chunk freshness tracking, and cost-per-query dashboards. A RAG system that worked on day one and drifts silently by month three is not a working system — it's a liability you haven't discovered yet.
What we build.
Hybrid retrieval pipelines
BM25 + dense vector search with query rewriting, re-ranking (Cohere, cross-encoders), and metadata filtering — tuned to your retrieval precision goals.
Evaluation harnesses
200–2,000 ground-truth QA pairs covering factual recall, out-of-scope refusal, multi-hop reasoning, and citation accuracy. Regression tests on every change.
Document ingestion pipelines
Parsers for PDF, Word, HTML, Notion, Confluence, Google Docs, Slack, and Jira. Semantic chunking, metadata extraction, and freshness scheduling.
Citations & source attribution
Every answer surfaces the exact source chunks it drew from — with page numbers, document titles, and confidence scores. No black-box answers.
Access-aware retrieval
User-level and role-level document permissions enforced at retrieval time — so engineers can't pull HR documents and contractors can't read internal financials.
Index drift monitoring
Continuous recall and precision checks against your ground-truth set. Alerts when a new document batch or embedding model update degrades performance.
Three ways to make AI know your business.
Fine-tuning gets suggested most and is usually the wrong first move. Here is why.
| Prompt only | Fine-tuning | RAG | |
|---|---|---|---|
| Updating information | Rewrite the prompt | Retrain the model | Change the document |
| Cost to keep current | Low but manual | High — every update | Low — it reads live |
| Cites its source | No | No | Yes |
| Handles a large corpus | No — context limit | Partly | Yes, this is the point |
| Time to first version | Days | Weeks | 2–4 weeks |
| Best for | A handful of fixed facts | Tone and format, not facts | Anything that changes |
What this is actually for.
Most support volume is the same twenty questions. Answered from your real policy pages, not invented.
New starters asking what is in a process document nobody can find.
Every response points at the source paragraph, so a human can check it in seconds.
How we build it.
Document audit
We review your knowledge base: format, quality, freshness, volume, and access model. We identify the top 50 questions users will ask and build a ground-truth eval set from them.
Chunking & indexing strategy
We test 3–4 chunking strategies (fixed, semantic, hierarchical) and measure recall against the eval set before choosing one. Chunk strategy is empirical, not a gut call.
Retrieval pipeline
We build the retrieval stack: embedding model, vector store, BM25, re-ranker, metadata filters, and query rewriter. We measure recall at every layer.
Generation & citation
We design the prompt, the citation format, the refusal behavior, and the out-of-scope handling. We run red-teaming to find hallucination patterns before launch.
Observability & tuning
We deploy with full query logging, relevance tracking, and cost monitoring. We improve weekly based on logged failures and user feedback.
Tools we use.
Frequently asked.
5 questions answered. Still have one? Reach out.
We build a ground-truth eval set of 200–2,000 question-answer pairs before we start building. We measure recall, precision, answer faithfulness, and citation accuracy at every iteration. RAGAS metrics are tracked in a dashboard from day one.