Eighty percent of what your organisation knows has never been in a system. We put it there.

Your experts' judgment, your institutional memory, the knowledge that actually drives decisions. None of it fits in a database. We capture it, structure it for AI, and build expert systems that get smarter every time they're used.

The problem

Your best people know things that aren't written down anywhere. The buyer who reads the market by instinct. The DBA who knows exactly which configuration fixes the problem. The engineer who remembers why that system was designed that way ten years ago. And the knowledge that is documented? It's buried in support tickets, spread across presentations, locked in forums and emails. Fragmented, unsearchable, disconnected.

AI can now change this, but only if you solve two problems that traditional approaches never have. How do you capture the knowledge that's never been written down? And how do you stop a knowledge base from going stale the moment you finish building it?

Capture what's never been written down

Our agentic interview platform is an autonomous AI agent that interviews your experts. It listens, follows narrative threads, asks genuinely incisive follow-up questions, and adapts in real time. Not just what your experts know, but how they think, what signals they watch for, and what experience has taught them.

Experts participate at their own pace. The output isn't a transcript. It's structured, contextualised knowledge, ready for AI ingestion.

We also ingest knowledge from across your entire organisation: support tickets, presentations, forums, documentation, internal communications. Everything your organisation knows, from every source, unified and retrievable.

Build a knowledge base that learns

Most RAG systems are static. Documents go in, get processed, and freeze. Nothing improves the knowledge base based on how it performs. Nothing flags when content goes stale. The knowledge starts degrading the moment you finish building it.

We build living knowledge bases. When an expert corrects an answer, the correction feeds back into the knowledge itself. When the system encounters a question it can't answer, it identifies the gap and reaches out to the right expert to fill it. Every interaction makes the system more accurate, more complete, and more valuable.

We wrote about why this matters →

What experts say about the interview platform

"That was cool as crap."

"Almost scary how natural that was."

"That was genuinely enjoyable."

This isn't a form to fill in. It's a conversation that people want to have. And that matters, because engaged experts share deeper, more valuable knowledge.

From knowledge to expert system

Capture expertise. Refine it for AI. Retrieve it intelligently. And keep improving it through use.

Capture

Expert interviews and org-wide ingestion

Refine

AI cleans, structures, and preserves traceability

Retrieve

Hybrid search finds the most relevant knowledge

Improve

Feedback loops make the knowledge base smarter

Capture

The interview platform draws out undocumented expertise through natural conversation. The ingestion pipeline pulls in everything else: documentation, support tickets, presentations, forums. All of it flows into the same knowledge base.

Refine

All ingested knowledge goes through our AI pipeline. The content is cleaned, structured, and converted into semantically coherent chunks that preserve meaning, context, and traceability back to the original source. Nothing is embellished or invented. Every piece of knowledge traces to the expert or document it came from.

Retrieve

Our hybrid retrieval combines vector search for semantic understanding, lexical search for precise terminology, and structured queries for factual data. A fusion layer combines the results to find the most relevant knowledge for any question. Your expert system handles nuance, understands context, and reasons about what you're actually asking. We wrote about why this architecture matters more than prompt engineering.

Improve

This is what makes a living knowledge base different. Expert corrections feed back into the content itself. Performance monitoring identifies chunks that are frequently retrieved but rarely helpful. Gaps are detected and filled through targeted expert interviews. The knowledge base doesn't just serve answers. It learns from every interaction and gets better over time.

Combined with live operational data

Knowledge AI works alongside our data platform. A DBA expert system that knows the manuals and can see your database performance. A buying expert system that knows the market and can see live futures prices. Expert knowledge grounded in real-time data.

Where this applies

From expertise to AI-first knowledge

Traditional knowledge management asks: "How do we document what our experts know?" We ask a different question: "How do we make what our experts know available to AI, permanently, and in a form that gets better over time?"

That's the difference between managing knowledge and becoming AI-first. Your expertise stops being something that depends on who's in the room, and starts being infrastructure.

Let's talk about the expertise you need to scale

Every business has knowledge that's too valuable to leave locked in a few people's heads. Tell us about yours.

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