Approach & technology

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.

The RAG pipeline

From question to verified answer

Six steps, one principle: the model may only speak about what it can substantiate.

1. Step 1

   Question

   The user asks a question in natural language.

2. Step 2

   Hybrid retrieval

   Vector (semantic) and keyword (FTS) search together surface the best candidate sources.

3. Step 3

   Reranking

   A reranking model puts the truly relevant passages on top — noise drops away.

4. Step 4

   Generation

   The language model formulates the answer, based solely on the retrieved sources.

5. Step 5

   Citation verification

   Is every claim actually in the source? If not, it gets cut.

6. 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.

Principles

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.

Under the hood

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.

pgvectorFTS / hybrid retrievalRerankingOllamaLlama · Qwen · GemmaCitation verificationFastAPIAstroCaddyCloudflaren8nschema.org · llms.txt

How we work

From idea to production — no pilot graveyard

01

Explore

We map out your process, data and goal. What can AI already take over, and where's the biggest payoff?

02

Build

A working prototype on your own data — RAG, agent or model — with citations and evaluation built in.

03

In production

Hosted where we agreed, monitored and maintained. No pilot that disappears into a drawer.

Deeper questions?

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.

Bekijk de showcases See a live product

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