Services · RAG & Knowledge
Every answer your company knows, one question away.
Years of documents, policies, wikis, and hard-won institutional knowledge — locked in folders nobody searches. We build retrieval-augmented generation (RAG) systems that let your team ask in plain language and get accurate answers with citations to the source document.
Why now
Information retrieval and decision support lead SMB pilots — RAG serves both.
of small businesses are piloting AI for information retrieval, and 41% for decision support — the two use cases RAG serves directly.
Upwork Research Institute, State of AI in SMBs, 2026of SMBs name data quality as a top AI barrier. Retrieval-first architecture grounds every answer in your actual documents instead of the model's guesses.
Techaisle, 2025 SMB & Midmarket AI Adoption Trends (n=2,100)What we build
Grounded answers with citations, from wherever your knowledge lives.
- Ingestion pipelines from wherever your knowledge lives: SharePoint, Google Drive, Slack, Confluence, wikis, email, PDFs
- Vector search and embedding infrastructure sized for your corpus and budget
- Grounded answers with source citations on every response — reduced hallucination by architecture
- Permission-aware retrieval: answers only draw on documents the asker is allowed to read
- Evaluation harnesses that measure answer quality on your questions before and after every change
“The test of a knowledge system isn't whether it sounds confident — it's whether it can show you the document it got the answer from.”
Process
How an engagement works.
- Free 30-minute consult. Tell us where your knowledge lives and who needs it; we'll sketch the retrieval architecture that fits.
- Pilot on one corpus. We ingest a meaningful slice — say, your policy docs or project archive — and let your team ask it real questions.
- Production rollout. Full ingestion, permissions, and deployment in your cloud, with evaluation baked in.
- Keep it current. Continuous sync as documents change, plus quality monitoring your team can read.
FAQ
Frequently asked questions.
What is RAG, in plain terms?
Retrieval-augmented generation means the AI first retrieves the relevant passages from your own documents, then writes its answer from those passages — with citations — instead of answering from generic memory.
Is RAG better than fine-tuning a model on our documents?
For company knowledge, almost always yes. RAG updates the moment a document changes, shows its sources, and respects access permissions. Fine-tuning bakes knowledge in at training time, can't cite where an answer came from, and must be redone as documents change.
Can it respect who is allowed to see what?
Yes. Permission-aware retrieval means answers only draw on documents the person asking is allowed to read.
Where does our data live?
In your environment. We deploy on your cloud accounts, and the model providers we build on (such as Anthropic and OpenAI) don't train on API data by default.
Sitting on years of documents nobody can search?
Grab time directly on our calendar, or send a note — we enjoy difficult questions and reply promptly.
Book a free 30-minute consultzanthos Inc. · Boston, Massachusetts
contact@zanthos.com