Process
A practical path from AI experiment to production automation.
Frumu starts with the operational process, then designs the workflow, permissions, integrations, evals, monitoring, and handoff needed for AI to run useful work safely.
How delivery works
The process is designed to surface risk early and make the production boundary clear before implementation gets expensive.
Discover
We map the business process, users, data sources, risks, and success criteria.
Design
We define the workflow, tool access, permissions, outputs, evals, and operational boundaries.
Build
We implement the automation system, integrations, runtime logic, UI, and backend services.
Test
We validate outputs, edge cases, failure handling, cost behavior, and security assumptions.
Deploy
We ship with monitoring, documentation, runbooks, and clear ownership.
Improve
We review results, tune workflows, expand capabilities, and remove bottlenecks over time.
What gets designed before launch
Production AI projects need clear boundaries around quality, access, visibility, and responsibility.
Reliability
Retries, fallbacks, state tracking, and recoverable execution.
Observability
Logs, traces, run history, and visibility into what happened.
Security
Scoped tool access, permission boundaries, data handling, and audit trails.
Evals
Regression tests, quality checks, and known-good examples.
Cost Control
Token limits, caching, model routing, and usage visibility.
Human Control
Approval points, review flows, and clear responsibility for sensitive actions.