JUBAP.Net, the organization behind the GEPLAN suite, is today a complex systems intelligence center focused on Operational AI Integrity and state-of-the-art early warning regime change detection. Its origins lie in the early 2000s, operating through The Integral Management Society in the United States and Corbera Networks in Mexico, where former Nokia R&D engineers built mission-critical systems for logistics-intensive environments such as PEMEX and regional transport operators.

An Installed Suite With a SaaS Mindset

GEPLAN was architected in an era before public cloud, commercial IoT platforms, and ready-made APIs, yet it behaved conceptually like an early SaaS for industrial operations. It was delivered as an on-premise, installable suite based on Delphi, ASP.NET, and MySQL, organized into interoperable modules for logistics operators, workshops, warehouses, fleets, and service stations. The catalog from 2008 already describes a coherent environment including inventory and budgeting, vehicle maintenance, logistics planning, fuel consumption control, volumetric management, ERP-style integration, remote access, and centralized operational reporting.

Because standard IoT stacks did not yet exist, the team relied heavily on reverse engineering to connect hardware, dispensers, telemetry devices, and legacy SCADA environments directly into GEPLAN. The result was a de facto pre-IoT integration layer, reinforced by extensive scripting and early RPA-style automation to move data between industrial devices, back-office systems, and field users without manual re-entry.

Modular Architecture, Pre-Microservices

Technically, GEPLAN adopted a modular architecture designed to be adapted for different clients and operational contexts, even if the language of microservices was not in use at the time. Modules such as GEPLAN/V for volumetric control could be deployed as part of a larger suite or as standalone components tightly integrated with existing infrastructure.

GEPLAN/V, for example, read multiple dispenser brands like Wayne and Gilbarco, integrated tank telemetry, tracked opening events and fuel levels, and connected to PEMEX workflows for transport, loading, unloading, invoicing, and franchise management. It consolidated POS, ticketing, inventory control, and service-station administration in one environment, acting as a practical control tower nearly two decades before industrial IoT platforms and commercial “control tower” marketing became mainstream.

Similarly, the fleet-control environment linked satellite telemetry, Omnitracs, dispatch centers, maintenance, fuel control, drivers, electronic work orders, evidence attachments, alerts, and estimation workflows into a single operational intelligence layer. This was more than GPS tracking; it was an integrated decision-support fabric for daily operations.

SaaS as Consulting-Plus-Software

What made GEPLAN particularly close to a SaaS model was not only its modularity but the way it was commercialized and implemented. Clients did not simply receive software licenses: they engaged in a structured consulting process that began with job-profile mapping, process discovery, and organizational re-engineering. In many cases, this journey led to ISO 9001-style process formalization as a first step, before fully tailoring and deploying the system to the refined operational model.

The suite was therefore “consumed” together with organizational consulting, with JUBAP.Net acting as both software provider and integrator of management practices. This consulting-plus-software approach aligned strongly with the meaning of The Integral Management Society: a focus on managing the whole system rather than a single tool or department.

Commitments Based on Value, Not Features

Commercially, the company structured its commitments around value creation instead of feature delivery. Contracts did not center on a checklist of functionalities; they focused on concrete business KPIs such as reducing inventory shrinkage, increasing the efficiency of scheduled trips, tightening fuel control, or improving fleet availability.

The promise to each client was that a given module or configuration would generate measurable operational improvements, not just additional screens or reports. This value-based orientation pushed the engineering teams to design GEPLAN as a living part of the operation, continuously refined through field feedback, rather than as a static IT product.

Over time, that discipline of tying technology architecture to real-world behavior became the foundation of JUBAP.Net’s current work in Operational AI Integrity and early warning regime change detection, where systemic shifts are identified not by abstract theory but by their concrete operational signatures.

Related case study: Mexico Before Industry 4.0: The Tegrity.AI GEPLAN Case