RAG development for LLMs that know your business.
Retrieval-augmented generation — done right. Chunking, embeddings, hybrid search, reranking and evals — so your AI answers from your data, not from the internet's guesses.
RAG (retrieval-augmented generation) development is the engineering of AI systems that combine a large language model with a search over your own data — using embeddings, vector databases, hybrid retrieval and reranking — so the model answers grounded in your documents.
What we deliver with RAG Development Services.
PDFs, Notion, Confluence, Drive, SharePoint, SQL.
Vector + BM25 + reranker — not just cosine similarity.
Golden sets, regression tests, human review loops.
Per-tenant indexes with strict isolation.
Fully local with open models and local vector DBs.
Honest fit check.
A plain answer up front. We'd rather not sell you something you don't need.
- Your LLM answers need to come from your documents, not the internet
- You need sources cited on every answer
- You care about accuracy evals on a real golden set
- You only need creative writing — RAG isn't what you need
- You want fine-tuning only — see LLM Integration or ML
- You won't do quality review or eval work — it's how RAG stays good
Our stack, battle-tested.
RAG vs fine-tuning vs long context
Starting from $700, depending on project scope and requirements. On-prem deployments with open models are scoped separately from SaaS-LLM builds.
What makes us different.
The people you meet in discovery stay involved through architecture, delivery and launch.
Metadata, schema, page performance and semantic markup are part of delivery, not a post-launch add-on.
Tradeoffs, integrations and scope changes are documented so your team can audit decisions later.
Repos, infra, analytics and documentation live in your accounts from the beginning.
Frequently asked questions
RAG for knowledge that changes. Fine-tuning for style, format or tight latency. Often both.
Usually: bad chunking, embedding-only retrieval (no BM25, no reranker), no evals, no source-attribution. Fixable.
Yes — Llama / Mistral + local vector DB + your own GPUs or CPU-only for smaller models.
We test multiple strategies — fixed-size, recursive, semantic and document-aware chunking — and pick the one that scores highest on your golden eval set. There is no universal best approach.
Yes — we ingest PDFs, Word docs, Notion, Confluence, Google Drive, SharePoint, SQL databases and structured APIs into a unified retrieval layer.
Separate vector indexes or strict metadata filtering per tenant so each customer's data is isolated, searchable only by their users, and never cross-contaminated.
Related services.
Ready to start?
Tell us about your project. A senior strategist replies within one business day — with a written first take.