LLMOps release workflow
Prompt/version control, evaluation, approval gates, deployment records, and rollback patterns for AI assistants and agents.
AI-ready DevOps and platform engineering
Ship AI safely with MLOps, LLMOps, and release governance
DigiScience modernizes DevOps only where it helps AI delivery: controlled releases, model/prompt evaluation, deployment approvals, observability, cost tracking, and secure platform patterns.
DevOps for AI outcomes
This is not generic CI/CD setup. It is the operating system for governed AI releases.
Delivery capabilities
MLOps and model lifecycle
Model packaging, validation, deployment, monitoring, drift review, and retraining workflow where ML models are required.
DevSecOps for AI platforms
Infrastructure-as-code, secret handling, policy checks, environment promotion, Kubernetes patterns, and operational monitoring.
When this matters
Use AI-ready DevOps when the buyer needs repeatable AI releases, governance evidence, production monitoring, and controlled experimentation.
Outputs
Release workflow, environment model, evaluation plan, monitoring dashboard, cost review cadence, runbook, and operating handover.
Recommended package: Governed AI Platform or AI Pilot Growth
Start lightweight for pilots, then mature the release pipeline when the AI workflow moves toward production use.
Discuss AI-ready DevOps