Retrieval-augmented generation sounds simple until the bot confidently cites the wrong policy.
Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.
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Tell us the stack, the problem and how you want to work. We confirm fit on a short call.
We send profiles of pre-vetted senior engineers who match your stack and domain. You interview and choose.
Your engineers onboard and start delivering without a long recruiting cycle, with working software at the end of each sprint.
Engagement models
Add one or two senior engineers to your existing team to close a specific skill gap. Your leads, our specialists.
A small senior team working only on your roadmap, with a lead who owns delivery.
A scoped build such as a RAG system, a model or a pipeline, delivered to an agreed outcome.
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FAQ
A RAG developer builds retrieval-augmented generation systems: pipelines that find the right passages in your own data and give them to a language model, so answers are grounded in your content rather than the model memory. The job covers ingestion, chunking, embeddings, retrieval tuning, prompting and evaluation.
Usually because retrieval returns the wrong passages, documents are chunked badly, or nobody measures accuracy. Our developers fix this with better ingestion, hybrid search, reranking and an evaluation set that catches regressions before users do.
Yes. We build assistants that show the documents and passages each answer came from, which is important for regulated teams and for user trust.
Yes. We deploy RAG with open models and self-hosted vector databases in your cloud, private cloud or on-premises environment when data cannot leave it.
A focused RAG assistant typically reaches production in 3 to 8 weeks, with working software demonstrated at the end of each sprint.
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