Map before build
We document systems, data classes, decision points and failure modes before choosing the automation path.
Production AI standard
Useful automation has to be controllable.
We design AI workflows with defined data access, human approval, operational monitoring and recovery routes. Your team can see which decisions are automated, which require review and how exceptions are handled.
Least privilege
Connections are scoped to the smallest practical set of records and actions, with separate credentials where appropriate.
Human authority
High-impact or low-confidence outputs pause for an accountable person. AI assists; your team owns the decision.
Observable operation
Important steps, errors and hand-offs are logged so a workflow can be monitored, investigated and improved.
Tested releases
Representative evaluation cases, edge cases and acceptance criteria are agreed before a production change.
Controlled recovery
Retries, fallbacks, rate and cost limits, alerting and a named recovery path are designed into the workflow.
Control lifecycle
Production readiness from discovery to operation.
01
Discover
Data-flow map, risk classification, success metric and ownership.
02
Design
Permissions, model/provider choices, retention, approvals and fallback behaviour.
03
Validate
Test dataset, failure cases, quality thresholds and stakeholder acceptance.
04
Operate
Monitoring, change record, incident path, reviews and improvement backlog.
What gets agreed for your build
System and data ownership
Provider and retention choices
Access and secret management
Human approval boundaries
Quality and confidence thresholds
Logging and alert recipients
Failure and manual fallback path
Documentation and change control