RAG that answers correctly — with sources.
We build retrieval-augmented AI search and assistants over your docs, help center and data: hybrid search, reranking, citations, and an eval set that proves quality before launch.
Julian Lopez · Founder & lead AI engineer · 3 years building AI for a 1M+ learner platform
Why most of these projects stall.
Confident answers that are wrong.
Plain vector search returns "similar" text, and the model fills the gaps.
It misses exact terms.
Plan names, SKUs and error codes slip through semantic search.
Nobody knows if a change helped.
Without a fixed set of test questions, every change is a guess.
What we build with RAG
Support assistant
Answers customers from your help center and docs, with links to the source — and hands off to a human when unsure.
Internal knowledge assistant
Lets your team ask policies, contracts, wikis and runbooks in plain English.
In-product search & Q&A
A cited assistant inside your app, aware of each customer's account and context.
Document Q&A
Question answering over PDFs, contracts and reports, with page-level citations.
Measured at every step. Evals, not vibes.
Ingest
Connect your sources, clean them, and chunk by structure — headings, tables, metadata.
Retrieve
Hybrid BM25 + vector search, fused and reranked so the right passages reach the model.
Answer
Grounded generation with citations, guardrails and an "I don't know" path.
Measure
Accuracy tested on your real questions, plus cost and speed tracked per request.
Priced up front, in writing.
RAG & AI search
- Fixed scope, timeline and price before you commit
- Milestones with written acceptance criteria
- Code, docs and quality tests are yours
AI Production Audit
Already have a prototype? In five business days we measure quality, cost and latency, and hand you a prioritized, fixed-price plan.
See what's in the audit →Introductory founding-client rates, for up to three portfolio partnerships — in exchange for a case study if the work produces useful results.
Send a project brief
Tell us what your assistant needs to answer, what data it uses, and what is failing today. We reply within one business day.
Questions, answered.
Which vector database do you use?
Postgres with pgvector by default — it keeps your data in one place and is cheap to run. If you already use Pinecone, Weaviate or another store, we build on it.
Which LLM?
We are model-agnostic: Claude, OpenAI or Gemini, chosen by quality, latency and cost on your eval set — and easy to switch later.
How do you measure accuracy?
We build an eval set — real questions paired with the right sources — and score the assistant on it before every change. You keep it after handoff.
Can it run in our cloud?
Yes. We deploy into your infrastructure and accounts; your data never needs to leave them.
Tell us what you're building.
Book a free 30-minute call. You'll get a straight answer — and, if it's a fit, a concrete fixed-price plan. Sometimes the answer is "you don't need us."