Move from a useful pilot to a production-grade system.
An operable, documented and monitored system with visible owners, controls and metrics.
- •A validated pilot
- •Criteria achieved
- •An operational owner
- •IT and security alignment
Production begins where the demo ends.
Deployment addresses what keeps the system working: access, availability, errors, ownership, adoption, cost and change.
Controlled integration
Connections to systems of record are documented and limited to what is needed.
Observable quality
Outputs, errors, approvals and costs are monitored over time.
Clear ownership
Teams know who uses, reviews, fixes and decides on changes.
Progressive improvement
Scope expands only after stable results on the first workflow.
Every stage must produce an actionable decision.
Harden the architecture
Access, environments, logging, recovery, performance, cost and continuity.
Production architectureIntegrate the workflow
Triggers, interfaces, permissions, notifications and approvals in existing tools.
Integrated workflowPrepare teams
Usage rules, training, documentation, support and an Otto feedback loop.
Adoption planMonitor and improve
Quality, usage, value, incidents, costs and new exceptions.
Operating dashboardDeliverables designed to move forward, not fill a shelf.
Production integration
Connections to tools and data according to approved access and ownership.
Operational controls
Logging, thresholds, approvals, escalation, recovery and incident monitoring.
Documentation and transfer
How it works, limits, ownership and useful operating practices.
Performance monitoring
Business, quality, usage and cost metrics observed after launch.
A production AI system is a service to operate, not a deliverable to forget.
Models, data and usage change. We track the signals that reveal quality drift, new exceptions or declining adoption.
Quality
Usage
Business value
Cost and incidents
Two operating environments where we focus our expertise.
What to clarify before moving forward.
Can you integrate with our ERP, CRM or CMMS?+
It depends on the APIs, access and constraints of the tool. Assessment and pilot phases verify the required integration before deployment.
How do you handle system errors?+
Critical cases have thresholds, approvals, escalations and traces. Autonomy follows operational risk, not a universal principle.
What happens to Otto after launch?+
Otto can support onboarding, gather feedback and structure new needs. Agentscium remains responsible for agreed changes.
Can we take operations in-house?+
Yes, when architecture, skills and contract allow it. Documentation and transfer should be planned from the start.
Let’s find the right place to start.
A 30-minute conversation to understand how you work, what is getting in the way and where AI could genuinely help.