AI that knows your business, not the internet

RAG & Knowledge AI

Generic LLMs don't know your business. We build the retrieval layer that does — over your knowledgebase, product docs, ticket history, internal wikis, codebases — with citations, eval, and the infra to keep it fresh as your content evolves.

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92%

Citation-grounded answers

vs. ~40% from raw LLM

10×

Faster than search

for ticket deflection use cases

24/7

Fresh content

auto-reindex pipeline

What we deliver

Concrete outputs — not vibes.

Every engagement ends with artifacts you own — running code, infrastructure, and the documentation to keep building on it.

Ingestion pipeline

Connectors to Notion, Confluence, Drive, GitHub, Slack, Zendesk, your KB — chunked and indexed continuously.

Retrieval layer

Hybrid search (BM25 + vector), reranking, scoping by audience and permissions.

Generation with citations

Answers anchored to source passages. Confidence thresholds. Refusal patterns.

Permissions model

User-aware retrieval — answers respect who the user is and what they can access.

Evaluation harness

Golden Q&A pairs, scored against retrieval quality and answer quality separately.

Freshness

Re-index pipelines and stale-content alerts so the knowledge stays current.

How we work

From brief to production.

A tight, repeatable path. You always know what's happening and what comes next.

Content map

Where the knowledge lives, who can see what, how often it changes.

Ingestion

Connectors, parsing strategy, chunking, metadata extraction.

Retrieval

Vector store, hybrid scoring, reranking, permission filters.

Generation

Prompt design, citation format, refusal patterns, fallback behavior.

Eval & ship

Golden set, continuous eval dashboard, then deploy with monitoring.

Stack

The tools we typically reach for.

Not prescriptions — we adapt to what you already run. Worth knowing what we’re fluent in.

AnthropicOpenAIpgvectorPineconeWeaviateCohere rerankerLangChainLlamaIndex

FAQ

Questions about RAG & Knowledge AI

ChatGPT's file upload works for ~50 pages. Real RAG handles thousands of documents, respects permissions, retrieves only what's relevant, gives citations, and lets you measure quality. It's a system, not a feature.
No. We design permission-aware retrieval — what the user can see in their normal access stays what the AI shows them. Audit logged.
Continuous ingestion with change-detection. New docs flow in, deleted ones flow out, summaries get refreshed on a schedule.
Yes. Every claim links back to a chunk, which links back to the source doc and a passage range.
Less than people think. Postgres + pgvector is plenty for most use cases. Pinecone / Weaviate / Qdrant become worth it past ~10M chunks or specific latency targets.
Yes. We've built RAG layers on Snowflake, BigQuery and Databricks. Architecture changes but the principles don't.