Zero Undeclared Allergens

The problem

Your allergen incident ends up in someone else's product.

An undeclared-allergen incident or cross-contamination in a formula sold as raw material propagates into the client's finished product and certification.

• A functional ingredient is sold as raw material. An undeclared allergen or a cross-contamination does not stay in your plant: it travels into your customer's finished product and into their certification.
• The causes rarely come alone: a formula version that changed, a minor ingredient weighed from the wrong container, a changeover cleaned in a hurry, a label left over from the previous run.
• Each control on its own may pass. What fails is that nobody checks that the formula, the actual weighing, the cleaning and the dossier tell the same story about the same batch.
• One incident means a customer claim, a recall in somebody else's market and a multi-year supply contract under review, long before anyone has found the root cause.

How it fits the IRIS system

Connect, Edge and Agents in four rings — if they disagree, JIDOKA AI stops the line.

Connect + Edge + Agents coordinated in 4 rings: the ERP's formula, the actual captured weighing, Edge-validated cleaning, and the batch's evidence-pack dossier; if they don't match, JIDOKA AI stops the line. SMED AI speeds up formula changeovers.

No single ring solves the problem, because each one already exists today in some form and incidents still happen. What changes is the cross-check: four independent sources that must agree, and a line that stops the moment they do not. SMED AI makes sure that extra rigor does not cost changeover time.

See the full IRIS architecture →

Before and after

The four rings and what each one closes

Ring	Cases it draws on	What it closes
1 · Released formula	Dosing scale tied to ERP (8) + lab sync (6)	The released formula and its declared allergens
2 · Actual weighing	Weighing sheet (1) + minor scale (2) + receiving (7)	What really went into the batch, lot by lot
3 · Cleaning and packing	Allergen changeover (10) + packing inspection (9)	No residue on the blender, no wrong label on the bag
4 · Batch dossier	Two-tap validation (5) + evidence pack (11)	A signed, auditable record per batch
JIDOKA AI stop	Cross-check of rings 1 to 4	Batch held on any allergen discrepancy
SMED AI on formula changes	Changeover preparation and sequencing	Faster formula changes without skipping a ring

Yearly incidents risking client claims or certification loss → zero. Discrepancies caught on the line, not at the client's plant.

Impact estimate

Impact estimate — to be validated with your numbers.

The block below is an estimate to be validated against your plant's actual data. We put it forward so the committee has an order of magnitude; we refine it during the assessment.

• Estimated payback 6-12 months, measured against the first incident avoided.
• Yearly incidents that put customer claims or certification at risk go toward zero, with discrepancies caught on the line instead of at the customer's plant.
• It protects the multi-year supply contracts that depend on your allergen record, which is often the real asset of a functional ingredients business and the first thing a customer reconsiders after an incident.
• And SMED AI shortens formula changeovers, so adding verification does not cost capacity on the blenders.

Payback 6-12 months against the first incident avoided · protects multi-year contracts — estimate to validate

And the fair question from the production manager

"Do we need all twelve cases before this works?" — no, and framing it that way would mean never starting. The rings are built in layers and each case pays on its own. Where the system reads documents, panels or labels it works on anchored tasks, where the best models drop below 1.5% error [1], and JIDOKA AI stops only on a verifiable discrepancy between sources, never on model uncertainty.

^[1] OpenAI paper "Why Language Models Hallucinate", 2025 — on the reliability of AI in anchored tasks.