The headset that turns supervisors into a data source

The problem

The sewing floor's best data source cannot type.

critical sewing-floor knowledge lives in the line supervisor's head and never reaches the system.

• The line supervisor knows which operation is the bottleneck today, which operator needs help with the side seam, which machine skips stitches after lunch.
• They walk the floor all shift with hands full — bundles, garments, a tablet — and nobody can ask them anything because they cannot stop to type. Recording means walking back to an office that is far from the line.
• That knowledge dies at the end of the shift, or survives as a two-minute handover in the aisle.
• So the efficiency data in the system shows that a line dropped, but never why. And the same problem gets solved again next week by someone else, with no memory of the first time.

How it fits the IRIS system

Connect in voice mode — a custom wake-word and the supervisor keeps walking.

Connect voice mode with a custom wake-word. The LLM structures the dictation as an incident linked to the active station/line/shift and replies by voice or push to the line lead's tablet.

The supervisor speaks to the system when they choose, without stopping and without a keyboard, the way they would talk to a colleague. The dictation becomes an incident tied to station, line and shift, and it lands next to the live line-efficiency data instead of in someone's memory. Over a few months, that becomes the history of each line: which operation fails, when, and what fixed it last time.

See the full IRIS architecture →

Before and after

Today's floor round versus the dictated round

Aspect	Today	With iLEAN Connect
Floor knowledge	Dies at shift's end	In the system in real time
Why a line dropped	Unknown in the efficiency report	Linked to the efficiency data
Supervisor's effort	Stop and type, or don't record	Speak while walking
Shift handover	Two minutes in the aisle	A structured summary per line
Reply to the supervisor	—	By voice or push to the line lead's tablet
Operators' conversation in workwear sewing	—	Not recorded

knowledge that dies at shift's end → enters the system in real time and cross-references live line-efficiency data.

Impact estimate

An enabling piece — no payback of its own, and stated as such.

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.

• No standalone payback: we do not put a range on this one, because its value depends on what your continuous-improvement program does with it. It is validated with your continuous-improvement lead.
• It is strategic value: the enabler that feeds continuous-improvement programs with the why behind the numbers.
• Floor knowledge that died at the end of the shift starts entering the system in real time.
• And it cross-references live line-efficiency data, so a drop has a cause attached. Over time, the same symptoms on the same operations stop being anecdotes and show up as patterns.

strategic value, an enabler for continuous-improvement programs. *Estimate to validate* with the continuous-improvement lead.

And the fair question from the production manager

“Is this a way to monitor the line?” — no: iLEAN listens to the supervisor, only after the wake-word, and only to what they choose to dictate; the operators' conversation is not recorded. Structuring a dictation into station, line, shift and symptom is an anchored task — the stations and lines exist in your records — where the best models drop below 1.5% error [1]. And the supervisor sees the structured incident and can correct it before it is saved.

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