Ingeniería nativa de IA que puedes someter a auditoría.

Stakeholder simulation

Before we build, we simulate the people and the real usage at scale, so the requirement is pressure-tested against how load, edge cases and human behaviour will actually hit it, not against an ideal demo.

Design & analysis

Architecture, model choice and a data strategy, all traced back to the spec. We decide what runs where, which model is small enough for the target, and what “good enough” has to mean before anything is trained.

Data gathering & preparation

Often the dataset does not exist yet. Sourcing, collecting, cleaning, annotating and versioning it is part of the build, so the model is trained and evaluated on data that reflects the real target, not a public benchmark.

Guardrails

The controls we add across the system: input and output guardrails, safety and domain policy, and the CI gates that block anything that fails them. Guardrails are engineered in, not bolted on afterwards.

Validation & UAT

A consistent regression suite re-runs on every model, prompt or data change, inside CI-gated MLOps, so nothing promotes until it passes. Where supervised learning is involved, evaluation is part of the pipeline, not a one-off.

Runs on the target, at scale

Deployed to the actual target system, on the edge, offline, at scale, with zero data egress. Monitoring feeds drift, cost and quality back into the loop, so the model stays validated in production, not just at launch.