When the assessment fits
Prompts change without a review record; test answers look good but there is no agreed evaluation set; a model upgrade changes behaviour; or nobody can identify the last usable configuration and its recovery limits.
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.
Need to change an AI application without losing control of its behaviour?
A working demo does not answer whether the next prompt, model or retrieval change should reach users. DigiScience offers a scoped assessment of one release workflow for teams in India and remote international engagements. The first decision is which evidence and controls are missing, and whether your existing delivery tools can provide them.
What we examine
Bring a non-confidential release diagram, the versions you track, examples of expected and unacceptable outputs, current approval rules and an outline of your rollback process. Detailed access and sensitive data handling are agreed separately.
The written decision
Receive a map of the release gaps, proposed acceptance checks, options using your current tools, a prioritized recommendation and an implementation brief. The brief identifies dependencies, decision owners to appoint and checks still requiring execution.
Separate three decisions
- What changed? Identify the code, prompt, model, retrieval and policy versions involved. A source commit alone may not capture the deployed behaviour.
- What would justify release? Agree representative examples and failure boundaries before comparing versions. Include human review where the result cannot be judged reliably by an automated check.
- What can recovery undo? Restoring a configuration does not reverse messages already sent or records already changed. Define containment and reconciliation alongside rollback.
Implementation may involve improving the existing CI pipeline, adding evaluation coverage, tightening approvals or changing the release design. A new platform is an option to evaluate, not the default recommendation.
Scope: the initial Solution Assessment is a written decision package. Building pipelines, running penetration tests, production changes and ongoing support require separate scope. No uptime, compliance or model-quality guarantee is implied.
Delivery capabilities
LLMOps release workflow
Prompt/version control, evaluation, approval gates, deployment records, and rollback patterns for AI assistants and agents.
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.
Discuss AI-ready DevOpsMove from a working demo to a release decision
Use concrete evaluation, rollback and operating criteria for one AI workflow.
Read the practical guide →