Paperless carcass grading log

The problem

The paper that defines the carcass travels slower than the carcass.

The grading log is filled in by hand right on the line; it can sit undigitized for hours, and any transcription error breaks traceability at the first link.

• Gross weight, net weight, grading code and carcass number are written by hand on the rail, with wet gloves and the line moving. That sheet is the first link of traceability for every animal that enters the plant.
• Between the grader's signature and somebody typing it into the system, hours go by — and a whole weekend when the kill closes on Friday.
• In that window the carcass physically exists, hangs in the chill room and can already be assigned to a cutting order, but it exists in no queryable system.
• And one transposed digit in a carcass number does not stay a typo: it breaks the chain at the first link, exactly where nobody looks for it later. By the time the mismatch surfaces, the meat is boxed and the only honest answer is that two records disagree and neither can be trusted.

How it fits the IRIS system

Connect in photo mode — the grader keeps writing on the same log.

Connect photo mode. A grounded LLM extracts gross weight, net weight, grading and carcass number, storing them in central memory before the carcass leaves the line.

It asks for no change on the rail: the grader fills in the log they already fill in and adds one photo when the batch closes. No terminal is installed at the grading station, no new sheet replaces the old one and nobody has to learn a screen with wet gloves on. That is usually why it is the first case approved in a plant that kills four species and has no appetite for anything new on the floor.

See the full IRIS architecture →

Before and after

The grading log today versus the grading log captured

Aspect	Today, on paper	With iLEAN Connect
Capture latency	Hours, a weekend if it closes Friday	Second zero
Carcass number	Retyped once or twice	Read from the original log
Gross and net weight	In a folder in the office	History queryable per species and day
A transposed digit	Found when the chain is already broken	Flagged as low confidence
Yield per kill batch	An estimate argued about	Calculated on the logged data
Grader's habit	—	Unchanged: one photo

hours of latency → zero second. Manual transcription with errors → direct capture from the original log.

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 4-9 months, depending on the daily carcass volume.
• From hours of latency — a whole weekend at the worst moment of the week — down to second zero.
• Transcription hours recovered from whoever types the log today, usually somebody in the office who never saw the carcass.
• And the grading mix per species stops being reconstructed at month end: it is data from the moment the batch closes.

Estimated payback of 4-9 months depending on daily carcass volume. *Estimate to be validated*.

And the fair question from the production manager

“What if it misreads a weight or a carcass number?” — reading fields from a log with a known layout is an anchored task, where the best models drop below 1.5% error [1]. There is a second net on top: any weight falling outside the physical range for that species is rejected instead of accepted, and the summary goes back to the grader, who confirms or corrects before anything is stored.

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