Why most LLM pilots fail in production
Most organisations have run an LLM pilot. Few have shipped one to production. The gap isn't a model problem — it's an architecture problem.
Priya Nair
Chief Technology Officer
Every week another organisation announces an LLM pilot. Chatbots for internal knowledge, contract review tools, code assistants, customer support automation. The demos are impressive. The production deployments are rare.
We've worked with enough enterprise AI teams to know what causes the pattern. It's almost never the model. It's everything around the model.
The reliability gap. Large language models are stochastic. Run the same prompt twice and you get different outputs. For internal demos, this feels like intelligence. For production systems, it's a bug. Enterprises need deterministic enough behaviour to trust outputs at scale — and that requires evaluation frameworks, output validation, and fallback logic that most pilots never build.
The hallucination problem is structural. Models confidently generate plausible-sounding wrong answers. The only fix is grounding: connecting the model to authoritative sources via retrieval-augmented generation, and building evaluation pipelines that continuously measure answer quality against known ground truth. Most pilots skip this because it's expensive and unsexy.
Latency kills use cases. An LLM call adds 2–8 seconds to a workflow. In a chat interface, users accept that. In a document processing pipeline that handles thousands of documents, it compounds into hours. Pilots run on samples; production runs on volume. The cost and latency profile looks completely different at scale.
The context window is not a database. Stuffing entire document libraries into a context window is a common pilot approach. It works at small scale. It fails on cost, latency, and accuracy at production scale. A properly designed RAG pipeline with a vector store, chunking strategy, and retrieval evaluation is not optional — it's the architecture.
The path from pilot to production requires the same discipline as any software system: testing, observability, versioning, and a clear definition of what 'good enough' looks like. Most AI projects treat this as a problem for later. It isn't.