AI Strategy for Production.
Define the workflow, data, security, deployment, and operating model before engineering starts.
Workflow.
How work runs today, before AI touches it.
Roles.
Who acts, and where the handoffs happen.
Knowledge.
The docs and data the system reasons from.
Models.
Which models fit, and the tuning needed.
Security.
Data, identity, access, and audit trails.
Deployment.
Cloud, VPC, or on-prem with runbooks ready.
What the strategy covers.
Business workflow. The work as it runs today, before AI touches it.
User and system roles. Who acts, which systems participate, where the handoffs happen.
Enterprise knowledge. The documents and data the system reasons from.
Model and training requirements. Which models fit, and the evaluation and tuning they need.
Integrations. The systems the AI reads from and writes to.
Security and governance. Data, identity, access, and audit — end-to-end.
Deployment model. Cloud, on-prem, or hybrid — against your data boundary.
Operating model. Team, runbooks, and observability that keep it healthy.
From workflow to defined AI system.
Origon maps the workflow, the systems it touches, the data it needs, and the controls it has to respect. From there, the team defines the AI system: agents, actions, knowledge access, model requirements, deployment boundary, observability, and managed operations.
5+ years in production
Running AI systems at enterprise scale.