Data & knowledge engineering
Data and knowledge engineering that answers with the source
Corporate knowledge your teams can query in plain language, and scattered data consolidated into something you can investigate and trust.
| The business problem | Knowledge locked in documents and in a few people's heads, data split across systems, and the same investigation rebuilt by hand every time |
|---|---|
| What we build | Advanced RAG, semantic search, corporate knowledge bases and MCP integrations; data consolidation, entity resolution, temporal correlation, knowledge graphs and anomaly detection |
| What it changes | Faster answers with the source attached, fewer repeated investigations, and decisions backed by evidence |
| Proof in production | TributoX, our tax intelligence product, runs on data ingestion, reconciliation, private memory and RAG in production, with 147 paying customers |
| Your data | Stays yours, inside your infrastructure, and is not used to train third-party models |
Answers you can audit
A knowledge system is only useful if people trust it. Ours answer from your documents and show where the answer came from, so a wrong answer is visible instead of plausible. For the underlying approach, see what RAG is and when you need a vector database.
From scattered data to a reconstructed event
Investigations stall because the facts live in five systems with five names for the same supplier. We consolidate the sources, resolve the entities, line the events up in time and flag what does not fit, so the question "what happened here?" takes minutes instead of weeks.
Built to run every day
Ingestion, permissions, evaluation and observability are part of the system from day one, not an afterthought. See how we handle enterprise data privacy.
Frequently asked questions
Will our data train someone else's model?
No. Your data stays in your infrastructure, and enterprise API tiers do not use it for training by default.
RAG or fine-tuning?
RAG when the problem is what the model knows, such as your documents and policies. Fine-tuning when the problem is how it behaves. Most knowledge problems are RAG problems.
Can it connect to the tools our teams already use?
Yes. We integrate knowledge bases with your systems and with AI tools through MCP, so answers show up where people work.
How do you know the answers are right?
We evaluate retrieval and generation separately on real questions, and keep measuring in production.
What is the problem costing you today?
Book a 30-minute discovery call. We size the problem, what it costs to solve and the net benefit.
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