The AI integration checklist for engineering teams below is what we run against every project at Resser Solutions before sign-off. If your project misses more than three of these, you're not ready to go live.
Data
- Data is accessible to the AI system through proper RBAC, not a service account that bypasses tenant boundaries.
- PII is identified and redacted before crossing service boundaries when needed.
- Retention rules for inputs and outputs are documented and enforced.
Eval
- Eval set of at least 100 cases covering common, edge, and known failure modes.
- Eval runs in CI on every prompt or model change.
- Regression on the baseline blocks merge.
Cost
- Cost telemetry tags every call with feature, tenant, user.
- Per-request and per-session cost ceiling enforced.
- Alerting when any cohort exceeds the budget envelope.
- Prompt caching enabled where the provider supports it.
Reliability
- Retry logic for transient errors with exponential backoff.
- Fallback chain to a secondary model on hard rate limit.
- Graceful degradation: feature explicitly off, not silently broken.
- Idempotency keys on any mutating call.
Observability
- Every call traced with input, output, model, confidence, latency.
- Audit log table for human review when needed.
- Dashboard for cost, latency, error rate, eval-pass rate, per feature.
Operations
- Kill-switch per tenant accessible to support without a deploy.
- Runbook for on-call: common failure modes and remediation steps.
- Prompt versioning: every prompt change is rollback-safe.
- Documented model swap procedure.
Security and compliance
- Provider DPA signed where applicable (GDPR).
- Sub-processor list updated and disclosed to customers.
- AI features disclosed in privacy policy.
- Customer opt-out path for data used in evaluation or fine-tuning.