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RAG Systems

Retrieval-augmented AI grounded in your own data — accurate, current and cited, with an evaluation harness so quality is measured, not guessed.

TIMELINE3–6 weeks

IDEAL FOR

Teams building search, assistants or copilots over their own documents and data.

STACK

pgvectorembeddingsOpenAI / Anthropicevals

citedanswers grounded in your data

// WHAT’S INCLUDED

Pull in and keep your sources fresh, from docs to databases.

Split and index content so retrieval actually finds the right thing.

Fast, relevant retrieval tuned to your domain.

Every answer points back to its source — no silent hallucinations.

Measure accuracy on real questions before and after every change.

Keep the index current as your data changes.

// HOW IT WORKS

Ingest and index your data into a retrieval layer.

Tune search and prompts for relevance and accuracy.

Measure quality with evals, then ship with confidence.

// WHAT YOU GET

✓Ingestion pipeline

✓Vector index

✓RAG API / endpoint

✓Evaluation suite

✓Citations & guardrails

// FAQ

We build citations and an eval harness specifically to catch and prevent that — quality is measured, not assumed.

It’s model-agnostic — OpenAI, Anthropic or open models, chosen for your accuracy, cost and privacy needs.

// RELATED SERVICES

Let’s talk about RAG Systems.

Book a 30-minute call — scope, timeline and a fixed-price range, no obligation.