AI platform operations
Keep your models and agents live, versioned and owned
Release models and agents the same way, observe them live, roll back fast and keep every change owned.
AI platform operations
Release models and agents the same way, observe them live, roll back fast and keep every change owned.
Trusted by enterprise AI teams
What’s changing in AI operations?
of enterprise GenAI pilots deliver no measurable financial return.
of agentic AI projects will be scrapped by 2027 over cost, unclear value and weak controls.
surge in enterprise GenAI spend in 2025, even as per-token prices fell.
Sources: 1. MIT NANDA, 2025. 2. Gartner, 2026. 3. Menlo Ventures, 2025 LLM market update.
Use cases
Find prompts and configs changing outside version control, then version, gate and track every change.
Version auditorEstate scannerEvaluation gates
Attribute token and compute spend per agent, team and feature, with an owner, before you cut it.
Cost attributorTrace analystBudget alerts
Draft runbooks from past incidents and docs, so every agent has steps, an owner and escalation.
Runbook drafterOn-call modelEscalation
Replay sampled traffic against the evaluation suite, so every change passes a threshold before it ships.
Replay testerGolden testsThresholds
Rehearse shadow, canary and blue-green releases in non-production, with a rollback tested before you need it.
Dry-run operatorCanaryBlue-green
Building from scratch?Talk to our Agent Engineering experts →
The path
AI services
We keep AI stable and on budget even after launch. FinOps Studio gives every wasted dollar a named owner. Governance manages its accountability.
AI platform operations1Platform IPAI services
AI transformationIndustries
Real Estate
CPG
Retail
Pharma & LS
Capital Markets
Private Equity
ML & Data Science
Agentic AI
AI Cloud & Infra
AI-Ready Data
AI Governance
App Modernization
AI Consulting
Decision Intel
Foundation
Agents built, deployed and run in production, with full audit.
Explore Agent Engineering →
Govern AI risk and cloud spend, with cost per use case.
Explore AI & Data Governance →Agent Studio
Every runtraced from trigger to tool call to action, with its cost and the person who approved it.
ML Studio
No silent driftin the models your agents call, with retraining before decisions slip.
Governance Studio
Every tokenof LLM and cloud cost tied to a use case, with audit-ready evidence behind each action.
A secure, governed Azure lakehouse powering AI insights across 30+ countries.
Bodhi series
A BODHI session on scalable data platforms for enterprise analytics and AI, with Siddharth Poddar (Polestar Analytics) and Pritam Debnath (Sysco).
In this session
An agent does the first pass at every step. A named person checks it and signs it off before the next step starts.
AI drafts; your team decides and merges.
Azure, AWS, GCP or Databricks. No data leaves your boundary.
No evaluation gate, no managed service. We don’t run blind.
Versioned, gated, observed and logged for audit.
Partner
Partner
Partner
FAQ
No. We map the playbook onto your existing tools and fill only the gaps.
No. An agent does the first pass; a named engineer signs off every step.
Nowhere. Agents run inside your own cloud tenancy and read evidence in place.
A readiness review returns an inventory, scores and draft runbooks for your riskiest systems.
Yes. Managed AI operations covers monitoring, evaluation, releases and on-call.
Bring one system, and we’ll score it against the six lenses.
Senior Vice President
Assistant Vice President