Line I

Strategy, measured pilot, and governance to scale

We prioritize use case, ROI, and risk before the big build. Pilot with metrics, LGPD, and operations designed from the start.

Pains we solve

  • Client wants 'AI' without use case — risk of pretty demo and zero ROI.
  • Proof worked in demo but scale stalls on cost, LGPD, or hallucination.
  • Nobody knows who approves prompt, model, or integration in production.

What we build

  • AI Plan with prioritized use case and estimated ROI
  • Limited proof with accuracy, latency, and cost metrics
  • Production architecture: human approval, observability, and rollback
  • AI usage policy, LGPD, and audit trail
  • MLOps/LLMOps roadmap and stack selection (cloud or on-prem)

How we deliver in practice

Rational AI investment

Pain

Cautious leadership; hype versus real operations.

What we build

AI Plan with prioritized use case, limited proof, and metrics before full system.

Outcome

Phase 2 approved with accuracy and cost evidence — not a trend slide.

Map before the fire

Pain

Point integrations; IT fights fires without criticality or owner view.

What we build

Process, data, and integration diagnosis: origin, destination, owner, frequency, and plan B — with quick-win prioritization and governance.

Outcome

Leadership and IT decide what to build first with shared map; serious agent or channel born on clear foundation.

AI that survives in production

Pain

Generic model hallucinates on sensitive data or industry jargon.

What we build

Architecture with usage limits, versioning, observability, cost per transaction, and rollback.

Outcome

Solution enters the shop floor with runbook; operations knows who approves, monitors, and reverts versions.

Ready to talk?

Tell us about your operation — we'll help you find the right starting point.

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