Every case study here documents a real engagement - the failure mode found, the approach taken, and the outcome measured. Clients are named where consent is confirmed. Where anonymisation is required, the technical detail is preserved so the work can be evaluated on its own terms
A RAG-based knowledge retrieval system for a cultural organisation. 30 books, two languages, and a high-stakes client handover - built with solid retrieval architecture but no production infrastructure around it.
An engineering team had built a RAG-based knowledge retrieval system for a cultural foundation - a chatbot designed to answer questions about historical philosophy across 30 books in two languages. They had delivered the initial build and were preparing for client handover.
The ProblemThe team came in asking for help with test queries. What the engagement actually required was an architecture audit first. The system had a solid RAG foundation - hybrid retrieval, cross-encoder reranking, LaBSE embeddings - but no production infrastructure around it. No input validation. No LLM fallback. No backup for the BM25 index stored as a single local file. Three critical failures waiting to happen, none of them visible without an audit.
ApproachBefore running a single test query, the architecture was assessed against production readiness criteria - what happens when it breaks, what the cost exposure looks like, where a determined adversary could manipulate outputs.
A 47-page audit report documented all 12 production gaps with severity classification, remediation recommendations, and a sequenced implementation plan. The team had a complete architectural picture before client handover - not a reactive fix list after a production failure.
A practicing dermatologist with a 4,000–5,000 page reference textbook. Finding specific treatment protocols took 10 to 30 minutes per query in a clinical workflow where time is constrained and accuracy is non-negotiable.
The client is a practicing dermatologist who works daily with a 4,000–5,000 page reference textbook. Finding specific treatment protocols, drug interaction data, or diagnostic criteria could take 10 to 30 minutes per query - unacceptable in a clinical preparation workflow where time is constrained and accuracy is non-negotiable.
The ProblemThe tools available - generic AI assistants, PDF search - either carried hallucination risk or were too slow to be useful. In a clinical context, a fabricated drug interaction or an unsupported dosing recommendation is not a retrieval error. It is a patient safety risk. The requirement was not a faster search. It was a retrieval system that could be trusted on every single output.
ApproachA closed-domain RAG system was built: no internet access, no generic model inference, retrieval constrained entirely to the indexed textbook - with source citations surfaced at the chapter, volume, and page level on every response.
Query time reduced from 10–30 minutes to under 10 seconds. Zero hallucinated outputs in production - every response is directly grounded in the indexed textbook with a verifiable source citation. Practitioners can cross-reference any AI output against the physical source in under 30 seconds.
Devverse Labs documents engagements in full - the failure mode diagnosed, the architecture decisions made, and the measurable outcome at handover. Where clients have consented to named publication, the complete engagement record is available. Where confidentiality requires anonymisation, the technical specifics are preserved: the framework applied, the gap identified, the production criteria met. What is never published is an outcome metric without a documented methodology behind it. Case studies here are evidence, not marketing.
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