RAG pipelines

Search and question-answering over your documents, tickets and databases, with sources cited in every answer.

Retrieval-augmented generation lets your team ask questions of thousands of documents and get answers with citations. We build the ingestion, indexing, retrieval and evaluation pieces so quality is measurable and access rules are respected.

What’s included

Document ingestion and chunking
Vector search with pgvector or Qdrant
Hybrid search and re-ranking
Citations and per-user access control
Evaluation sets and quality tracking
Private or on-premise deployment

How we deliver it

Step 1

Discover

We learn the business, the users and the constraints, then write a scope with clear boundaries.

Step 2

Design

Wireframes, then high-fidelity screens and a clickable prototype you can put in front of users.

Step 3

Build

Two-week sprints with a working demo at the end of each one and a staging link you can always open.

Step 4

Launch & support

Release, monitoring and a support window, then ongoing improvement if you want it.

Questions we get asked

Can it respect who is allowed to see what?

Yes. Access rules are applied at retrieval time, so people only get answers from documents they could already open.

How do you measure quality?

We build an evaluation set from real questions and track answer accuracy and citation correctness every time the pipeline changes.

How much does it cost?

Every project is scoped in a short discovery phase. You get a fixed price per milestone, or a monthly rate for a dedicated team — no open-ended hourly billing.

Do you offer support after launch?

Every build includes 30 days of support. After that, most clients move to a monthly retainer for fixes, updates and improvements.

Let’s talk about RAG pipelines.

Share a few lines about the project. You’ll get a reply from an engineer, not a sales script, within one working day.