An AI That Answers From Your Data, and Shows Its Sources
We build retrieval-augmented generation (RAG) systems that turn your documents, databases, and know-how into an assistant your team and customers can actually trust.
Hybrid retrieval combines semantic vector search, exact keyword search, and knowledge graphs (Graph RAG), so the system finds the right passage whether someone asks in plain language or by part number.
From $7,500 · typically 3 to 6 weeks
Where RAG Pays Off
Internal knowledge assistant
Policies, SOPs, and past project notes in one place your team can ask. New hires stop interrupting senior staff with the same questions.
Customer support assistant
Answers grounded in your help docs, product specs, and policies, with a hand-off to a human when it is not sure.
Document search and review
Find the clause, the spec, or the precedent across thousands of contracts, reports, or case files in seconds.
Sales enablement
Instant answers on pricing rules, product fit, and past proposals, pulled from the documents your team already has.
Why We Build Hybrid
Vector search alone is the default in most tutorials, and it is why so many RAG demos fall apart in production. Each retrieval method catches what the others miss.
| Retrieval method | Good at | Misses |
|---|---|---|
| Vector (semantic) search | Questions phrased differently from the source text | Exact names, codes, part numbers, and rare terms |
| Full-text (keyword) search | Exact terms, IDs, and names | Paraphrases and synonyms |
| Knowledge graph (Graph RAG) | Relationships across documents: who, which, connected to what | Free-form passages with no clear entities |
| Hybrid (all three, re-ranked) | Accurate retrieval across all of the above | Needs careful tuning, which is the work we do |
Included in Every RAG Build
- Data audit: what sources exist, what is stale, and what should be excluded
- Ingestion pipeline for PDFs, Word, Excel, email, web pages, and databases, with scheduled refresh
- Chunking and metadata designed around your documents, not a default setting
- Hybrid retrieval with re-ranking on Postgres + pgvector, Qdrant, Pinecone, or your existing database
- Knowledge graph extraction where it measurably improves answers
- An evaluation set of real questions, with retrieval and answer scores before and after launch
- An interface: web chat, Slack or Teams, an API, or an MCP server for Claude and other AI tools
- Access controls, source citations, and documentation
We Run Hybrid Graph RAG on Our Own Data
RAG Questions
What is RAG, in plain terms?
Retrieval-augmented generation. Before the AI answers, the system searches your documents and data for the relevant passages and gives them to the model. The AI answers from your information, not from guesswork, and can show which source each part of the answer came from.
What is Graph RAG and do I need it?
Graph RAG adds a knowledge graph: a map of the people, products, clients, and documents in your data and how they relate. It answers questions that span several documents, like "which clients on the old contract template have renewals this quarter." Not every project needs it. We test whether it improves answers on your data before recommending it.
Why not just upload files to ChatGPT?
That works for a handful of files. It breaks down with thousands of documents, data that changes daily, information that needs access controls, or answers that must be accurate and cite their source. A RAG system is built for those conditions.
Is my data safe?
Your data stays in infrastructure you control. We can use AI providers that do not train on your data, or open-weight models running on your own servers when nothing may leave your network. Access controls can limit who can retrieve which documents.
How do you know the answers are good?
We build an evaluation set of real questions from your team with known correct answers, and measure retrieval and answer quality against it before launch and after every change.
What Should Your AI Know?
Tell us what documents and data you have and who needs answers from them. We will outline the system and the cost.
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