AI GCC in India · MLOps, LLMOps & AgentOps
Run production AI reliably. One operating layer for models, LLM applications and agents covering CI/CD, continuous evaluation, observability, model routing, drift, cost per task and governance integration.
MLOps, LLMOps and AgentOps give an AI GCC the production operating layer for machine-learning models, LLM applications and autonomous agents. They cover evaluation, deployment, observability, versioning, permissions, cost, governance and incident management. NeoIntelli builds this layer on the client's existing engineering toolchain and can operate it as part of a managed or Build-Operate-Transfer engagement until the GCC team owns it.
Architecture
Code, data, models, prompts and agents move through the same loop. The loop is what turns a pilot into a production capability.
STEP 01
Code / Data / Model / Prompt / Agent
Every artifact versioned and traceable.
STEP 02
Evaluate
Automated evaluation gates in CI for models, prompts, retrieval and agents.
STEP 03
Deploy
Governed release with rollout, routing and rollback.
STEP 04
Observe
Quality, drift, latency, cost, task success and escalations in production.
STEP 05
Govern
Inventory, approvals, incident handling and audit evidence fed back into the lifecycle.
Capabilities
01
Registry, versioning, lineage, approval and retirement for classical ML models, with training reproducible from code and data.
02
Prompts, templates and retrieval configurations versioned, tested against evaluation sets and released like code.
03
Agent registry, tool permissions, version control for plans and tools, and release gates that measure task success before an agent gets more scope.
04
Pipelines that validate data, train or adapt, evaluate, package and deploy, extending the client's existing DevOps toolchain rather than replacing it.
05
Evaluation sets run on every change and on a schedule in production, with human calibration for judgments an automated judge gets wrong.
06
Tracing across model calls, retrieval and tool use, quality and drift monitoring, and dashboards that engineering, product and governance read from the same source.
07
Latency budgets, fallbacks, routing across models, capacity reservations and incident response for AI systems as production services.
08
Cost per model, per prompt and per task, routing and caching decisions backed by data, and reviews that keep production spend tied to value.
09
Risk classification, approvals, documentation and evidence generated by the pipeline, so governance is a property of the platform rather than a meeting.
Metrics we expose
No generic deployment multipliers or savings percentages. These are the measures the platform exposes, with targets agreed per engagement.
Targets agreed per engagement. Not benchmarks, not guarantees.
The compute these systems run on is planned under AI Infrastructure & GPU. The controls and evidence the loop produces are governed under AI Governance & Responsible AI.
Buyer questions
AgentOps is the operating discipline for autonomous and semi-autonomous AI agents in production: registry, versioning, tool permissions, tracing, evaluation, human escalation, cost per task, incident handling and governance evidence.
It extends LLMOps in the same way LLMOps extended MLOps. Agents act on systems, so the controls are closer to those of a service and a user account than to those of a model endpoint.
MLOps runs the lifecycle of trained models. LLMOps adds prompts, retrieval, evaluation of generated output, routing and cost per call. AgentOps adds tools, permissions, multi-step traces and task-level success. An AI GCC needs all three as one operating layer.
Yes, scaled to the portfolio. Even a handful of production systems need versioning, evaluation gates, observability and rollback. What changes with scale is tooling depth, not whether the discipline exists.
The client's existing CI/CD and cloud tooling wherever possible, extended with registry, evaluation, tracing and cost tooling chosen for the workload. The platform is designed to be owned by the GCC, so tool choice also considers the skills the team will have.
Yes. Under the Managed and Build-Operate-Transfer models NeoIntelli can run the MLOps, LLMOps and AgentOps layer with agreed service levels and hand it to your team at transfer.
Tell us what is in production, what is stuck in pilot and how releases happen today. We will map the lifecycle, evaluation, observability and cost layer the GCC needs to run it.