Food Manufacturing
Every lot ships with a certificate. Not every certificate was worth trusting.
How a food manufacturing company replaced a manual, error-prone QA validation process with an AI assistant on Forra that gives specialists a complete, trustworthy report in seconds, without removing them from the decision.
The challenge
Repetitive validation work, hiding a real compliance risk.
The client must attach a Certificate of Analysis to every lot they ship. No COA, no delivery. Before any certificate could be issued, a QA specialist had to manually validate the data behind it: open two large Excel workbooks, scan conformity flags across hundreds of palettes, cross-reference moisture and water activity measurements against product specs, and verify that lab results for E. coli and Salmonella were present and conforming.
Every lot. Every time. Manually.
The process was low-value work for a specialist whose expertise is better spent on exceptions: the non-conforming palettes, the borderline results, the judgment calls. But because the validation was manual, it was also inconsistent. A result that was easy to catch on a focused morning could slip through at the end of a busy day. Some mistakes made it as far as the customer.
There was also a deeper trust problem. The quality tracking file had known reliability issues: the production date it recorded could be wrong, which had previously forced a full revalidation pass after a system update. The specialist had learned to work around it. But that kind of institutional workaround is fragile, and invisible to anyone new to the role.
The solution
Human-in-the-loop by design.
Mirego built a QA validation assistant directly in Forra, designed around one principle: remove the mechanical burden without removing the specialist from the process. Forra handles the validation. The technician reviews the result, asks questions if something needs clarification, and signs off. The operator stays accountable. The repetitive work disappears.
When validation runs, the assistant doesn’t just return a pass or fail. It produces a full breakdown of every check performed: which palettes were conforming, what the moisture and water activity values were, what the lab results showed for each sample. If something fails, the report names exactly what failed and points to it. If something warrants attention without blocking validation (a borderline value or a non-applicable measurement), it surfaces that too, so the technician is never caught off-guard downstream.
The assistant supports two triggering modes. It can run on a schedule, automatically scanning for lots released in the production system and ready for validation. Or a technician can trigger it ad-hoc by typing a product code and lot number directly in the chat. Both modes produce the same structured, complete report.
The outcomes
The mechanical work is gone. The specialist’s judgment isn’t.
- What used to mean opening two large workbooks and scanning hundreds of rows is now a report the technician reads in seconds.
- The validation runs the same way every time, against live data, with no steps skipped, regardless of workload or time of day.
- Edge cases that were easy to miss manually, like a single non-conforming sample buried in a multi-sample lot or a missing lab result, are caught consistently.
- When something fails, the report says what, where, and which palettes are affected. The specialist knows exactly what to look at without retracing the validation themselves.
- Lots released overnight are already validated by the time the team arrives in the morning, with no one having to remember to check.
- Because the report shows actual values and reasoning, the technician can trust the process, not just the result, and move forward with confidence or flag a concern knowing exactly what prompted it.
Your knowledge base isn’t the problem.
How it is searched or used, is.
Purpose-built AI agents replace a generic assistant, achieving 87.5% accuracy on a validated benchmark versus 12% for Microsoft Copilot.
The power of experience: capturing 30 years of knowledge.
AI transcribes expert interviews, identifies speakers, and turns institutional knowledge into a searchable resource for your entire team.
The answer existed. It was buried in 15 hours of searching.
AI research agent searches four scientific libraries simultaneously and delivers a complete, sourced report in minutes instead of hours.