// 02 · Service
RAG systems development
Retrieval grounded in your own data, with an eval harness that proves the answers are faithful to the source — not plausible-sounding guesses.
- pgvector
- Postgres
- LlamaIndex
- OpenAI
// The problem
Why this is hard
A vector database and a prompt get you a demo; they don't get you answers you can trust. Real retrieval systems fail on chunking, on recall, and on faithfulness — the model confidently cites something the source never said. Without an eval harness you can't tell a good answer from a plausible one, and in a knowledge system that's the whole game.
// What we build
What you get
Ingestion & chunking
A pipeline that turns your documents into retrievable, well-chunked, up-to-date context.
Hybrid retrieval
Vector + keyword search tuned for recall on your corpus, not a generic default.
Grounded synthesis
Answers composed only from retrieved sources, with citations back to the exact passage.
Retrieval eval suite
A harness that scores faithfulness and citation support on every change, so quality is measured.
// How it fits together
The system we build
- Ingestdocs → chunks
- Indexvector + keyword
- Retrievehybrid, tuned recall
- Synthesisecited, source-only
- Eval gatefaithfulness scored
// Deliverables
- Ingestion & chunking pipeline
- Hybrid vector + keyword search
- Grounded answer synthesis
- Retrieval eval suite
// How we work
From prototype to production, in four moves.
Discovery
We map the problem, the data, and the eval that defines "done".
Prototype
A working slice in weeks — real model, real data, measured.
Production
Hardened, observable, evaluable. Shipped where users live.
Scale
Cost, latency and reliability tuned as load and scope grow.
// Typical engagement
What it takes to work together
Architecture Sprint
$4–8kfixed · 1–2 wks
1–2 weeks
De-risk before you build — architecture, a plan, and a working proof-of-concept.
Build
from$20kfixed scope or pod
typically 1–3 months
Ship the product end-to-end, in your repo and conventions.
Run & Scale
from$4k/ month
ongoing
Operate and improve after launch — SLA, monitoring, and iteration.
Indicative ranges — the final price and timeline depend on your project's scope, complexity, and integrations. A paid Architecture Sprint pins them down.
// Proof
Shipped in production
Healthtech — Clinical RAG
hallucinated citations in eval
on the citation eval set
source-linked answers
every shipped answer
// FAQ
Common questions
// Related
AI Agents & Automation
Tool-using agents that act on your systems — calling your APIs, running workflows, deciding what to do next — with guardrails, retries, and traces so they are safe to run unattended in production.
LLM Integration & Evals
Models wired into your product behind an interface you control, provider-agnostic by design. Every prompt and model change is gated on evals — measurable quality, not vibes.
Product Engineering
Full-stack, AI-native products from first prototype to a system users trust — designed, built, and shipped in your repo and your conventions.
MLOps & Infra
The queues, workers, observability, and cost controls that keep models fast, reliable, and on budget once real traffic arrives.
AI Strategy & Audit
A clear-eyed read on where AI pays off and where it does not — an architecture review, an honest risk and feasibility assessment, and a prioritised roadmap before you commit budget.
// Industries
Where teams put this to work:
// Let's build
Ready to build with rag & knowledge systems?
Tell us where you are. We reply within a day with a concrete next step.