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ChatGPT on your own data, without the hallucination embarrassments. Retrieval pipelines, enterprise knowledge assistants, and agentic RAG — engineered (chunking, hybrid search, reranking, evals), not plugged in.
Every team wants "ChatGPT on our data." The difference between a demo and a system your staff trust is retrieval engineering: how the corpus is chunked, how hybrid search is tuned, how results are reranked, and how accuracy is measured — continuously, on your real questions.
We build RAG the way we build products: a Sprint first ($10K / 4 weeks) that audits your corpus, stands up a retrieval prototype on your real data, and — most importantly — establishes an accuracy baseline on questions your team actually asks. Production builds then land in the AI MVP or Modernisation tiers, with evaluation and monitoring shipped as part of the system, and your data staying in your cloud.
Our own proof: PenLeap's rubric-scoring pipeline retrieves over curriculum content for 10,000+ students at a measured 95% pass rate — retrieval quality is the product there, and it transfers.
Every engagement establishes an eval set from questions your team actually asks. You see retrieval quality as a metric before rollout.
Chunking strategy, hybrid keyword+vector search, reranking, and freshness pipelines — the unglamorous work that separates trustworthy from embarrassing.
PenLeap retrieves over curriculum content for 10,000+ students — retrieval quality is the product, measured at a 95% pass rate.
Agentic RAG connects retrieval to actions — the assistant that finds the policy can also file the claim, gated and audited.
We leverage the latest technologies and frameworks to deliver robust, scalable solutions.
Our proven process ensures successful project delivery every time.
What you have, where it lives, what questions it must answer, and what "correct" means — written down before anything is built.
A working assistant over a slice of your real corpus, with chunking and search strategy tuned on your content.
An accuracy score on real questions, failure analysis, and a build recommendation with production costs stated.
Full corpus, freshness pipeline, monitoring, and rollout — in the AI MVP or Modernisation tier, fixed price.
Let's discuss your project and find the perfect solution for your business needs.