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 creates the engineering and operating patterns required for controlled AI releases: versioning, evaluation, approvals, deployment, rollback, observability, security evidence, and cost tracking.
The operating system for AI releases
Connect experimentation, evaluation, infrastructure, security, governance, deployment, monitoring, and operational ownership.
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.
Production release path
Version and evaluate
Version code, prompts, models, retrieval configuration, datasets, policies, and infrastructure; run automated and human evaluation suites.
Approve and deploy
Apply security and policy checks, evidence gates, environment promotion, canary or controlled rollout, deployment records, and rollback.
Observe and improve
Monitor quality, groundedness, safety, latency, errors, drift, usage, cost, incidents, user feedback, and improvement actions.
Industrialize the path from AI change to governed production
Start lightweight for a pilot and mature the release, evaluation, evidence, monitoring, and operating model as the workflow moves toward production.
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