Why enterprise AI experiments too rarely become production systems
In this point of view, Salim Laimeche argues that the challenge is no longer proving that a model can answer. The challenge is building AI systems that answer accurately, cite sources, respect permissions, and survive production.
The point is no longer to show that a model can answer. The point is to prove that an AI system can answer accurately, cite sources, respect permissions, and hold up in production.
A conviction built from the field
IgnitionAI was founded by Salim Laimeche, an AI engineer with experience at BNP Paribas CIB, Sanofi, and Brevo, a Packt Publishing author, and a certified TensorFlow Developer and Azure AI practitioner. Across these environments, the same pattern appeared: AI demos can impress quickly, but reliable systems require a different discipline.
For large organizations and for consulting firms or agencies delivering document AI to their own clients, the blocker is not only model choice. It is data quality, access control, traceability, execution cost, integration into business tools, and the ability to measure answer quality over time.
Why RAG projects fail in production
Most RAG projects do not fail because the model is bad. They fail because the company has not industrialized everything around the model.
Fragmented data
PDFs, emails, exports, presentations, business files, and internal databases remain scattered. Without a robust ingestion pipeline, AI sees only part of reality.
Answers that cannot be audited
An unsourced answer may work in a demo. In production, teams need citations, context, and a clear path back to the original document.
Access rights left behind
Document AI must respect permissions, organizations, public collections, and private collections. Otherwise it becomes a risk, not a tool.
Poor model and cost governance
Model configuration, reranking, embeddings, and usage limits must be controllable by the platform. The bill cannot be a surprise.
Dependency on custom delivery
Too many projects remain trapped in bespoke stacks. Every prompt, model, or workflow change becomes an expensive intervention.
Not enough observability
Without traces, tests, canaries, quality metrics, and workflow replay, a team does not know whether its AI is improving or regressing.
What IgnitionRAG changes
IgnitionRAG brings together the building blocks needed to move from a demo to a manageable document AI platform.
Public proof, not just a promise
This press page stays careful with non-public references. The proof points below are therefore public or explainable without creating false commercial signal.