Hire Senior RAG Developers
A RAG demo is easy. A RAG system that returns the right context, does not hallucinate, and stays accurate as your data grows is engineering. We staff senior engineers from our own team who build the second kind. Start in about five days, prove fit in a two-week paid trial, exit any time on 30 days.
Where RAG projects actually break
The tutorial works on ten documents. Production breaks on chunking strategy, retrieval quality, stale embeddings, and the lack of any evaluation harness to tell you whether answers are getting better or worse.
That gap between demo and dependable is exactly where most RAG efforts stall - and where our engineers start.
What our engineers own
Ingestion and chunking, embedding and vector storage, retrieval and re-ranking, grounding and citation, and an evaluation loop so quality is measured, not hoped for. Wired into your product, not a notebook.
How it works
A short readiness audit scopes your data and the retrieval problem. A two-week paid trial confirms fit on real work. If it is not right, you exit on 30 days - no recruiter fees.
Proof
Answers grounded on the client’s own data - accurate enough to beat their built-in report engine.
On Dr. Todd Hall’s platform, our engineer built AI that generates interactive reports grounded strictly in the organization’s own survey and assessment data, not in a model’s guesses. It runs in production and outperforms the platform’s built-in engine across a pipeline that includes a 1,800-student university and a 3,000-staff organization.
Read the case studyStack
Frequently asked questions
- Can you build RAG on our private data?
- Yes. Grounding models on your own documents and data - with the data model and access controls you require - is the core of what we build.
- How do you keep answers accurate?
- An evaluation loop plus retrieval re-ranking and citation, so quality is measured and improved rather than assumed. Guardrails keep the model from answering beyond its context.
- Which vector stores and models do you work with?
- We are stack-flexible - common vector databases and the major model providers, including Claude through MCP - matched to your constraints rather than a fixed toolset.
- How do you prove the answers stay accurate as our data grows?
- We build an evaluation harness against your real data and questions, so accuracy is measured, not assumed. You see the numbers before you scale, and you own the harness. If the trial does not convince you, you exit on 30 days.