Knowledge search
Your organisation already knows the answer. It's in a PDF, in a folder, that someone left in 2022.
Institutional knowledge with no index
Every organisation accumulates knowledge faster than it can organise it. Policies, procedures, past projects, technical documentation, meeting notes, the email thread where a decision was actually made.
The information exists. It's just unfindable, because keyword search requires knowing the words the author used, and your new starter doesn't. So they ask a colleague, who interrupts their work to answer, or they guess.
Retrieval-augmented generation — RAG — closes that gap: search by meaning, answer in plain language, cite the source so the reader can verify.
The technique is well understood. The engagement is in the details, which is where these projects succeed or fail.
What we build
- Internal knowledge assistants
- Question answering across your documentation, policies, and past work, respecting who's allowed to see what.
- Customer-facing documentation search
- Support and product content that answers rather than returning a list of ten links.
- Research and analysis tools
- Search across large document collections for teams whose work is reading: legal, compliance, technical, academic.
- Contextual in-product help
- Answers surfaced inside your software, aware of what the user is doing.
Where these projects actually fail
- Chunking
- How documents get split determines what can be found. Split badly and the answer is spread across two fragments that never surface together. This is unglamorous and it's the single largest driver of quality.
- Permissions
- If retrieval ignores access control, your knowledge assistant will cheerfully summarise the salary review a user isn't entitled to read. Permission-aware retrieval has to be designed in from the start, not filtered afterwards.
- Stale content
- Confidently citing a superseded policy is worse than no answer. Documents need freshness handling and a path for retiring old material.
- No evaluation
- Most RAG projects launch without a test set, so nobody can say whether a change made it better. We build a question set from real queries — with correct answers agreed by your team — and measure against it every time we change anything.
- Conflicting sources
- Two documents disagree, because organisations are like that. The system should surface the conflict rather than silently pick one.
Before you ask.
Documents, wikis, ticket histories, code, transcripts, databases — most things, with connectors to the systems they live in.
Chatbots & Assistants
Your customers have met a chatbot before. Statistically, it wasted their time. You're not starting from neutral — you're starting from a grudge.
AI & MLNatural Language Processing
Your customers don't write like your training data. They abbreviate, misspell, switch languages mid-sentence, and describe your product using words you've never used for it.
Intelligent SystemsDocument Processing
Somewhere in your business, a person is reading a PDF and typing what it says into a form. Possibly right now.
Tell us what you're trying to build.
A 30-minute call, no charge and no pitch deck. Describe the problem and we'll tell you how we'd approach it, roughly what it costs, and whether we're the right team for it. If we're not, we'll say so.
30 minutes · No charge · No deck