Production-grade RAG engineering. Demo to 94% accuracy.

Most RAG systems work in demos. They fail in production.

We know because we've been on the call when a VP forwards a wrong AI answer to three of their reports. That's the moment you realize your 62% baseline accuracy is a business problem, not just a technical one.

Ailoitte builds the retrieval infrastructure that closes that gap.

Not a different LLM. Not prompt engineering. The actual retrieval layer: semantic chunking, hybrid search, cross-encoder re-ranking, source hierarchy, and structured evaluation pipelines. The unglamorous architecture work that separates demo RAG from production RAG.

We've taken systems from 62% to 94% accuracy, with the same model, in six weeks.

Who it's for: AI product teams, CTOs, and knowledge management leads who have a RAG system in production and know it's not as accurate as it needs to be.

What it costs: $20K–$45K, depending on scope. We start with an accuracy audit, run your system against real user queries, measure the gaps, show you exactly what's failing and why, before we propose any build.

We're a small team. We take 2-3 projects at a time. We've seen enough production RAG failures to have a clear playbook.