The alert that used to arrive late, now arrives on time

The problem

The warning that changes the week arrives in somebody's personal inbox.

A supplier of a microbial strain or a plant extract flags a delay or a change in the received batch's concentration. Or a regional distributor flags a field incident over WhatsApp. That alert lands in a personal inbox read hours later, when there's already a work order planned with that raw material or a batch already shipped to the field.

• A supplier of a microbial strain or a plant extract warns of a delay, or of a change in the concentration of the lot already received. That changes the dose of the next formulation.
• Or a regional distributor reports a field incident over WhatsApp: a grower complaining that a biopesticide did not perform, a jerrycan that arrived swollen. Those reports are the earliest signal of a batch problem, and today they depend on someone forwarding a chat.
• Those messages land in a personal inbox or phone and are read two to three hours later, when a work order using that raw material is already planned or a batch has already shipped.
• With live cultures and short-shelf-life extracts, a late reaction is not just a sequence change: it can mean dosing a lot whose viability has already dropped.

How it fits the IRIS system

Connect in chaotic-sources mode — silent copy on the supplier channels, around the clock.

iLEAN Connect sits in silent copy on the supplier email or WhatsApp. In zero seconds it extracts the intent, cross-checks active work orders in the ERP, and suggests the concrete action to the right role.

Reading the message sooner is not enough. What counts is knowing which work orders use that strain lot, how many days of coverage are left and who has to move. That is what comes back, already crossed with the ERP, to the person who can act. With live cultures, those hours are the difference between adjusting a dose and losing a batch.

See the full IRIS architecture →

Before and after

The strain warning in an inbox versus the warning acted on

Aspect	Today, in the inbox	With iLEAN Connect
Time until someone decides	2-3 hours	Seconds
Concentration change in a received lot	Discovered at dosing	Flagged against the work orders using it
Days of strain coverage	Calculated by hand, if at all	Shown with the alert
Field incident from a distributor	Stays on a phone	Tied to the shipped batch
Who receives it	Whoever the sender knew	Planning, quality or purchasing, by topic
Who reschedules	The planner — iLEAN proposes	The planner — iLEAN proposes, with data

Before: 2 to 3 hours until someone decides what to do. After: rescheduling in seconds, based on real-time data. Estimated payback 3 to 6 months depending on how often critical alerts arrive.

Impact estimate

Impact estimate — to be validated with your record of supplier incidents.

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 3-6 months, depending on how often critical alerts arrive.
• From two to three hours of waiting to minutes to reschedule a batch, based on real-time data instead of memory.
• Fewer formulations dosed with a strain or extract lot whose concentration had already changed, which is where a batch becomes nonconforming without anyone noticing.
• Field incidents reported by distributors linked to the right batch from the first message, instead of being reconstructed weeks later from screenshots and memory.

Estimated payback 3-6 months · from hours of waiting to minutes to reschedule a batch.

And the fair question from the production manager

“Will it read the purchasing team's mail?” — no. iLEAN sits in copy on a dedicated supplier mailbox and a company messaging number, and only sees what reaches those operational channels. Classifying whether a message is a delay, a concentration change or a field incident is an anchored task where the best models drop below 1.5% error [1] — and the rescheduling is decided by a person.

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