Reliability for models and workflows

We treat prompts, retrieval policies, and classical models as versioned artifacts. That means reproducible training and evaluation jobs, promotion gates, and monitoring that fires before customers notice regressions.

Automation layer

• Event-driven orchestration with explicit SLAs and compensating transactions.
• Human-in-the-loop steps for low-confidence model outputs or high-risk actions.
• Feature flags and shadow traffic for safe rollout of new model versions.
• Cost dashboards tying token usage, GPU hours, and infrastructure spend to products and teams.

Operations

We document incident response for model failures: who gets paged, how to roll back, and how to freeze bad training data from polluting the next deploy.

How it Works

• Evaluation harnesses with regression suites checked on every change
• Data and model lineage from warehouse tables to deployed endpoints
• SLOs for latency, error rate, and business KPIs tied to alerts
• Workflow observability: step timings, retries, and business outcome metrics

Qualifications & Requirements

• Baseline metrics and acceptable error budgets from product and risk owners
• Access to production logs or a logging platform for alert routing
• Change management process if you operate in regulated environments