Two taps before touching the ERP

The problem

The plant wants the data captured and is afraid of what it will write.

The main cultural brake on digitization is the fear that AI will enter data on its own and contaminate the master ERP. At the same time, operators don't want to type or waste time manually validating every capture either.

• The main cultural brake on digitizing an avocado plant is not the technology: it is the fear that AI enters data on its own and contaminates the master ERP.
• A wrong orchard on a lot, a wrong ripeness grade or a wrong destination does not stay in a report — it follows the fruit all the way to the export container, and correcting it there costs days.
• At the same time, nobody on the line wants to type or spend minutes validating every capture in the middle of the season.
• Between those two fears, the usual outcome is the worst one: the data is not captured at all, out of distrust, and the plant keeps running on paper and memory through its busiest months.

How it fits the IRIS system

Connect with early human verification — a two-second summary, two taps.

Connect early human verification mode: every capture -scale photo, voice incident, parsed orchard alert- lands on an industrial tablet at the line as a 2-second visual summary. The line supervisor confirms with 2 taps (check to enter the ERP / edit to correct first).

This is the piece that makes the other eleven signable. Quality and IT do not approve AI writing into the ERP; they approve a supervisor confirming, in two seconds, what the AI has already assembled. That shift in who writes, and how, is what unlocks the budget for the rest.

See the full IRIS architecture →

Before and after

Unchecked capture versus capture confirmed at the line

Aspect	Today	With iLEAN
Who writes into the ERP	Nobody trusts the answer	The line supervisor, by confirming
Time per capture	Minutes of typing, or nothing at all	A 2-second summary, two taps
A wrong orchard or ripeness grade	Travels with the fruit	Corrected before it enters
Sources covered	Each one handled apart	Scale photos, voice notes and grower alerts
Master data	Contaminated or incomplete	Clean, with seconds of latency
What is stored	The final value only	The value, who confirmed it and what was edited

Unvalidated data contaminating the ERP, or data not captured due to distrust → data captured and validated with seconds of latency, clean master data.

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.

• No standalone payback: it is the enabling piece of the whole matrix, with strategic value that is not directly monetizable.
• Without early human verification, the rest of the pieces do not get signed off by quality or IT, so its value is counted in every other case of the matrix.
• Its measurable effect shows up in the other cases: data captured with seconds of latency instead of retyped hours later.
• And master data that stays clean because every entry has a named person who looked at it, which is what an export customer or an auditor ends up asking for.

The enabling piece for the whole iLEAN matrix: without early human verification, the rest of the pieces don't get signed off. *Strategic value, not directly monetizable.*

And the fair question from the production manager

“If a person confirms everything anyway, what does the AI actually save?” — the assembling, not the deciding. Reading a scale ticket or a dictated note is an anchored task where the best models drop below 1.5% error [1], so the summary usually arrives right and the supervisor only confirms. When it is wrong, the edit is stored with its author and becomes feedback for that exact point. Over a season, the share of summaries that need an edit keeps falling.

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