Custom business management software

It includes process analysis, data model design, operational UX/UI, modular development, permissions, audit, workflows, reporting and integration with the current stack. Additionally, it's delivered with QA and controlled deployment. We approach it in phases so the project has clear milestones: first an MVP that solves a bottleneck, then scaling.

It depends on the scope, but the usual is 8-12 weeks for an operational MVP. The important thing is to define the MVP well: the minimum that generates real value and allows validating operations. If the project is more complex, it's divided into phases with intermediate deliverables.

Yes, that's the recommended approach. Starting with one module reduces risk, accelerates results and allows validating the data model and operations before expanding. The important thing is to design the foundation well (data, roles, traceability) so the system can grow without rework.

Yes. We usually integrate via API/webhooks or batch synchronizations, depending on the operational impact. We define the "source of truth" for each data (where the customer data lives, where inventory lives, etc.) and apply validations and traceability so the integration isn't fragile.

We work in phases and each phase has a closed scope. Changes enter a prioritized backlog and are evaluated by impact on cost, time and risk. This avoids "feature creep" and maintains schedule control. If something is important, it's incorporated; if not, it's left for a later phase.

Yes. We can offer corrective and evolutionary maintenance, plus monitoring if the environment requires it. The SLA and response times depend on the contracted level and system criticality. The recommended approach is to have a retainer for improvements and continuous adjustments once the system is in production.

We apply role-based access control, action audit and good data processing practices (minimization, traceability, access control). If the system handles sensitive data, it's reinforced with additional measures as needed (stricter access controls, segmentation, etc.).

It depends on the type of system and its requirements. The important thing is to choose a stable, maintainable stack aligned with operations: backoffice, integrations, reporting and scalability. The decision is documented in the Blueprint and aligned with future maintenance and evolution.

First we audit and clean data: duplicate fields, different formats and inconsistent records. Then we define a mapping to the new data model and migrate in stages to reduce risk. Usually it's validated with a "dry run" before the definitive change.

An internal process owner, access to current systems (even if read-only), clear objectives and priorities. With that, a Discovery can be started to define MVP, risks and roadmap without improvising.