What is RAG and when should I use it?

Retrieval-augmented generation gives a language model access to your private data at query time. Use it whenever your users need answers from your docs, knowledge base, or product catalogue — not from the model's general training data.

RAG vs fine-tuning — which should I pick?

RAG for fresh, changing data and when you need citations. Fine-tuning for strict output format or brand voice. Most products start with RAG and fine-tune only the gaps. We cover this decision in depth on our blog.

How accurate is a production RAG system?

With a reranker, hybrid retrieval, and a tuned eval set, our pipelines land at 88–95% retrieval accuracy on client corpora. Without a reranker, expect 10–20 points lower. We measure and report this weekly.

How long does it take to build?

A production-grade RAG system takes 4–8 weeks depending on corpus size and access-control complexity. A working prototype with basic retrieval ships in the first two weeks.

What does it cost to run?

Embedding is a one-time cost (<$100 for most corpora). Per-query: retrieval hop ~$0.0001, LLM call $0.005–$0.02. Total ops at 10k queries/month: $200–$500. No GPU needed.

Can you build RAG over our existing Postgres?

Yes — pgvector turns your existing Postgres into a vector store. No new infrastructure, no new vendor. This is our recommended path for teams already on RDS or Supabase.

Service / RAG Development

Retrieval-augmented generation that answers from your data, not the internet.

We build production RAG systems that give your LLM access to your private docs, knowledge base, or product catalogue — with citations, access control, and accuracy you can measure. Not a weekend vector-DB demo — a pipeline your customers actually trust.

Book a discovery call See the work

What we build

A production RAG pipeline, not a prototype.

Every component — from chunking to citation UI — built, measured, and deployed as a system.

Chunking and embedding pipeline

Recursive chunking with overlap, OpenAI or Cohere embedders, metadata extraction — tuned per corpus, not left on defaults.

Vector store selection and setup

pgvector if you already run Postgres, Pinecone or Qdrant for scale. Schema design, indexing strategy, and hybrid (BM25 + dense) retrieval.

Reranking for accuracy

Cohere Rerank or cross-encoder models that lift retrieval accuracy 10–20 points over cosine-only search. The step most teams skip.

Citations and source attribution

Every answer links back to the source chunk with page/section references. Your users — and your compliance team — see where facts come from.

Access control and multi-tenancy

Org-level and user-level ACLs on the retrieval layer so each customer sees only their data. Critical for B2B SaaS RAG.

Eval harness from day one

100-query scored eval set built in week one. Every chunking, embedding, or prompt change is measured against it before merge.

How we build it

Corpus to production in 4–8 weeks.

No discovery phase that never ends. Each step has a deliverable, a date, and a demo.

Corpus audit

We profile your documents — format, length distribution, structure, metadata density — and pick the chunking + embedding strategy that fits.

Pipeline build

Ingest → chunk → embed → store → retrieve → rerank → prompt → generate. Each hop instrumented, each step configurable.

Eval and tune

Run the 100-query eval set. Iterate chunking, prompt, and reranker until accuracy clears the bar your team sets.

Ship with guardrails

Production deploy with citation UI, hallucination detection, fallback paths, and observability (LangSmith or Langfuse).

Pipeline metrics

Numbers from real client deployments.

30+

RAG systems shipped

92%

Avg. retrieval accuracy

2 wk

To first working pipeline

<$0.02

Per query at 10k/mo

RAG stack

The retrieval infrastructure we deploy.

Opinionated defaults — we swap components when your corpus or scale calls for it.

OpenAICoherepgvectorPineconeQdrantLangChainLlamaIndexLangSmithLangfusePythonTypeScriptNext.js

FAQ

RAG: accuracy, cost, and production readiness

Next step

Let's scope your rag development build.

A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.

50+: MVPs shipped
8 wks: Avg. delivery
$20M+: Raised by clients
30 days: Post-launch support

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