No answer without a source. And the source is verifiable.
A language model that sometimes makes things up is worthless for serious work. Our entire pipeline is built to prevent that — and to make it demonstrably true. This isn't a marketing promise; it's how the system is put together.
From question to verified answer
Six steps, one principle: the model may only speak about what it can substantiate.
- Step 1
Question
The user asks a question in natural language.
- Step 2
Hybrid retrieval
Vector (semantic) and keyword (FTS) search together surface the best candidate sources.
- Step 3
Reranking
A reranking model puts the truly relevant passages on top — noise drops away.
- Step 4
Generation
The language model formulates the answer, based solely on the retrieved sources.
- Step 5
Citation verification
Is every claim actually in the source? If not, it gets cut.
- Step 6
Answer + source card
The result comes with a clickable reference to the original.
Continuous feedback. An evaluation harness with a fixed set of checked questions (golden set) re-measures quality and the hallucination rate with every change. Regressions become visible before they hit production.
Four choices that decide everything
Citations by design
Sources aren't bolted on afterwards — they're built into the foundation. An answer without a verifiable source doesn't make the cut.
Your data, your place
For each part of the pipeline we agree where it runs and why. You do not get a promise, you get an overview of which part sits where.
Layered AI = low cost
RAG and vector search are nearly free; a local model handles the heavy lifting; an expensive API is only called in where it's truly needed.
Measured, not hoped for
An evaluation harness with a golden set objectively measures quality and hallucination rate. We steer by the numbers and keep them below market leaders.
Mature, proven technology
No black-box platform locking you in, but a transparent stack we keep in our own hands — and one you can run yourself if you want to.
From idea to production — no pilot graveyard
Explore
We map out your process, data and goal. What can AI already take over, and where's the biggest payoff?
Build
A working prototype on your own data — RAG, agent or model — with citations and evaluation built in.
In production
Hosted where we agreed, monitored and maintained. No pilot that disappears into a drawer.
Want to know if it works on your data?
The honest test is a trial setup on a slice of your own data. We show you what the retrieval surfaces, what the model makes of it, and what the source card looks like.