Production-ready AI architecture — not just for demo
Usage limits, human approval, observability, cost, and rollback — operations knows who approves and monitors.
Pilot impresses in presentation but stalls in production from hallucination, cost, or LGPD. An architecture designed for shop floor defines scope limits, human approval on sensitive transactions, prompt and model versioning, accuracy and cost-per-transaction observability, and rollback when new version worsens. Operations receives clear runbook — who approves, who monitors, how to revert. AI enters routine with governance, not black box IT fears to touch.
What blocks you today
Generic model hallucinates on sensitive data or industry jargon. Nobody knows who approves prompt, model, or production integration. Cost explodes at scale; rollback is manual or nonexistent. Operations distrusts and returns to spreadsheet.
What changes in practice
- Reference architecture with usage limits, profiles, and configurable human approval
- Observability: accuracy, latency, cost per transaction, and exception queue
- Prompt and model versioning with tested rollback
- AI usage policy, LGPD, and audit trail integrated into design
- Runbook for operations and IT — deploy, monitoring, escalation, and reversal
Business outcome
Solution survives shop floor with measurable success criteria. IT releases evolution with confidence — doesn't block everything from black box fear. Operations knows who approves exception and how to revert bad version before damage.
Where it usually fits
- Companies with validated pilot needing to scale with governance
- Regulated operations — health, finance, agro, industry — with sensitive data
- IT requiring observability and rollback before opening to real users
- Leadership that approved AI but wants cost and risk control in production
- Teams already burned by chatbot or disposable POC after go-live
How it evolves next
With closed architecture, rollout follows to expanded pilot, continuous MLOps, or new modules on same governance layer.
- Live Pilot with minimal integration on defined architecture
- Scale with Control by waves and monitored SLA
- Integration map updated as new connectors enter
- Domain fine-tuning evaluation when generic model doesn't close
- Additional agent or copilot reusing same approval and log layer
Generic model hallucinates on sensitive data or industry jargon?
Nobody knows who approves prompt, model, or production integration? Let's talk — diagnosis and proof before the big investment.