Knowledge Base (RAG)
Your documents start answering questions
Every answer links back to where it came from, and we agree on a test set with you, so you see the quality numbers before handover.
The knowledge exists. Getting it out is the problem.
Most companies have plenty of documentation. What they lack is a way to ask it a question.
Answers live in someone's head
Two or three experts answer the same questions all day. When they're on holiday, or they leave, that knowledge walks out with them.
Search finds files, not answers
Keyword search returns forty documents. The person asking needed one paragraph from page 12 of the third one.
Documentation exists but isn't used
Wikis, PDFs, meeting notes, chat threads. Written once and then never found again, so people just ask a colleague because it's quicker.
What you get
A knowledge system whose answers you can check and whose quality you can measure, on a test set you agreed to before we built it.
- Documents that answer questions
- An assistant over your own material: wikis, PDFs, tickets, chats, even expert video, which we've turned into structured knowledge before.
- Every answer cites its source
- Each answer links to the exact passage it came from, so anyone can check it before they trust it.
- Quality measured before delivery
- We agree a test set with you up front, the real questions that matter, and you see the actual numbers before handover.
- Says when it does not know
- It's built to say so when the answer isn't in your documents, and the test set measures how often it gets that call right.
How we get there
We start small, prove the quality, and widen from there. No company-wide rollout on day one.
Pilot on a limited corpus
We start with one document set and one group of users, not a company-wide rollout.
Agree the eval set
Together we collect the real questions that matter to you and agree what a correct answer looks like.
Measure, then expand
The assistant ships when it clears the agreed bar on the eval set. Then we extend coverage corpus by corpus.
We've built these in production
Knowledge systems we shipped in production, plus an open, AI-judged RAG result anyone can check.
In production
Expert Blockchain Chatbot
Production RAG with quality we measured. Retrieval goes past plain vectors (SQL, entity lookup, real-time data), and an evaluation framework (RAGAS) scores the answers.
Read the project →In production
AI Stylist: Fashion Attributes from Expert Video
Expert video turned into structured, queryable data, backed by the product's first labeled garment-image dataset.
Read the project →In production
Jazion: An AI Copilot for Language Tutors
Turns recorded lessons into structured, queryable knowledge: a summary, lesson card, Q&A, and quizzes delivered to students automatically.
Read the project →In production
AI Book Recommendation Assistant
Answers come from a live product database. Hybrid retrieval (RecSys, vector search, and LLMs) does the work, so the model isn't guessing from memory.
Read the project →Open code
ARLC 2026 Legal RAG Challenge
3rd of 155 teams in the challenge's warm-up phase, scoring 0.954/1.0. An agentic RAG pipeline that answers questions over real DIFC legal documents, with every answer graded by AI judges. The code is open, so anyone can check it.
View the code ↗
Frequently asked questions
Every answer links to its source passage, so you can check it on the spot, and before handover we measure quality against a test set we agreed with you, so you see accuracy numbers rather than adjectives.
Language models can get things wrong, and anyone promising zero hallucinations is overselling. We make the errors detectable and countable: answers grounded in your sources, a test set we agree on, and a measured rate for how often the assistant correctly says it doesn't know when the answer isn't in your documents.
Your documents are used to build your knowledge base and nothing else. How it's deployed, including keeping everything inside your own infrastructure, is part of scoping. We don't use your data to train anything shared.
Text first: wikis, PDFs, docs, tickets, chat history. We've also turned expert video into structured knowledge (see the AI Stylist project below), and connected structured databases when questions need live facts (see the blockchain assistant).
It depends on the size of the corpus and the questions you need answered, so we don't quote a number before scoping. The pilot comes first, priced fixed after a scoping call, which is what the form below kicks off.
Not sure a knowledge base is the right first step? Start with the two-week agent build.
Pilot first, fixed quote after scoping
Discuss your case
Tell us what knowledge you'd like answering questions, and we'll come back with some scoping questions of our own.