AI proposes, deterministic logic disposes: the pattern that keeps AI production-ready

Yann Gilliot, founder of GiLabs, described a three-layer production architecture in which AI systems only propose outputs — OCR, language models and extraction tools attach confidence scores to every proposal — while a deterministic business-rules layer of tested .NET code validates each proposal and a human decides on low-confidence or rule-violating cases. Gilliot said the document pipeline he delivered in an industrial context holds up because AI mistakes are structurally blocked before reaching a production database, and argued the key production question is what happens when the AI gets it wrong.

TL;DR AI in production does not decide. It proposes, to a system that has the right to say no. Every AI system I have put into production shares this same three-layer pattern. AI proposes. OCR, a language model, extraction: it reads the document or the input data, detects, extracts, and attaches a confidence score to every proposal it makes. It never writes directly to the database. Business rules validate. A deterministic layer, made of ordinary code, testable and auditable, checks every proposal the AI makes: expected formats, consistency against existing reference data, business thresholds. What passes these checks gets accepted. What fails is blocked before it reaches the system. A human decides. A confidence score that is too low, a business rule that gets violated, a case never seen before: the proposal goes into a validation queue, and a person decides. Every human decision is stored and fed back into the system, which improves with use this way, without heavy model retraining. In this architecture, a model's hallucination is no longer a diffuse, uncontrollable risk. It is an explicitly handled case. An AI that gets something wrong becomes a proposal rejected by the validation layer, never wrong data reaching a production database directly. This is exactly how the document pipeline https://gilabs.fr/en/blog/pipeline-idp-production/ I delivered in a demanding industrial context runs day to day. It holds up over time not because the AI never makes a mistake, but because its mistakes structurally have nowhere to go without passing through a check. In a system like this, the part actually occupied by artificial intelligence is small. Everything that makes the system trustworthy comes down to ordinary software engineering: tested .NET code, a structured SQL database, explicit business rules, automated tests. That is what I build in a .NET AI integration https://gilabs.fr/en/services/dotnet-ai-integration/ : AI inside the existing code, under its rules. I am often asked which model I use for a given project. That is almost never the most useful question. The question that actually matters is: what happens when this AI gets it wrong? Sometimes the answer allows full automation, as on the Convention Online AI chain https://gilabs.fr/en/blog/convention-online-ia-autonome/ , where a mistake is fixed on the next pass. If the answer is not clear before the first deployment, the system is not yet ready for production. Originally published on gilabs.fr https://gilabs.fr/en/blog/ia-propose-deterministe-dispose/ . I'm Yann Gilliot, founder of GiLabs https://gilabs.fr/en/ . GiLabs helps SMEs, mid-caps and professional firms succeed in their AI transformation: process mapping, a costed roadmap, then building the agents and applications that run in production, wired into what you already have.