Real results.
Real plants. Real data.

iGromi OS is running in production across plants in Chile. These are not simulations or proofs of concept: they are documented cases with measurable outcomes and client authorization.

Poultry / FoodCustomer: PROA

99% counting accuracy measured against manual counting

Problem

Manual egg counting at more than 17 process points, reconciled at shift end with batch age and genetics. The figure arrived a week late.

Infrastructure

19 existing security cameras across 4 farms, with one industrial GPU PC per farm. Integrated with SAP S/4HANA.

Replaced manual counting entirely and has been in production for two years. Production data reaches SAP without transcription or spreadsheets.

What the platform learned: Packaged SAP S/4HANA connectors and poultry ontology templates reused with new customers.

¹ Internal measurement against manual counting, validated with the customer. Case documented with authorization.

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Agribusiness / Fruit and vegetablesCustomer: DITZLER

Real-time production count and temperature

Problem

Production and process conditions recorded by hand, with no continuous reading of temperature or electricity per machine.

Infrastructure

In-line counting sensors, calibrated process temperature probes and per-machine energy meters connected to iGromi OS. Sensor readings, no cameras.

Sensor counting, process temperature and energy per machine on one board, with a continuous history instead of manual records.

What the platform learned: The catalog Energy and Climate Lobes install without custom development: the customer meters are data, not a new project.

¹ Customer in production. Process figures under confidentiality.

MetalworkingCustomer: RGM MALLAS DE ALAMBRE

Real-time output per machine and per recipe

Problem

Per-machine output with no automatic record. Each mesh type has its own recipe and supervisors had no shared reading across shifts.

Infrastructure

Counting sensors on the mesh machines, per-product recipes standardized across every machine and supervisors trained on the platform. Periodic sensor maintenance.

Each machine reports units and pace against the recipe of the running product. Supervisors compare shifts without spreadsheets.

What the platform learned: A heterogeneous machine park is standardized by recipe, not by machine: the same logic serves all of them.

¹ Customer in production.

Cosmetics / PackagingCustomer: INTERCOS

Real-time units per shift and stops per machine

Problem

Units per shift and machine stops recorded by hand by the supervisor, with no shared reading between production and IT.

Infrastructure

Counting sensors on the packaging machines connected to iGromi OS with the MES module: units per shift, stops per machine and Excel export. Sensor readings, no cameras.

Each machine reports output and stops to the plant board. The customer IT team accesses the platform and the exportable data.

What the platform learned: Sensor-based MES installs without computer vision or Edge AI: one counting kit per machine plus the subscription is enough for a packaging plant.

¹ Customer in production. Process figures under confidentiality.

Customers in production: PROA · Ditzler · RGM Mallas de Alambre · Intercos

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