AI-first GCC
Model lifecycle management, CI/CD for ML, experiment tracking, monitoring, drift detection, and cost discipline - so your AI systems run reliably and improve continuously in production.
10x
deployment frequency
<1 hr
detection of model drift
100%
reproducible training
30%+
infra cost reduction
An AI model is a perishable asset. The data shifts, the world shifts, user behaviour shifts, and the model's accuracy quietly decays. Without MLOps, this decay is invisible until a business team complains. By then, trust is lost and the model is hard to recover.
LLMOps adds another set of concerns - prompt versioning, evaluation pipelines, model routing, hallucination tracking, and cost-per-call observability. Traditional MLOps stacks were not designed for this. Production GenAI needs an evolved operating layer.
NeoIntelli builds the MLOps and LLMOps operating layer that gives enterprise AI teams reproducibility, automated testing, continuous evaluation, monitoring, drift detection, and cost discipline - so production AI compounds instead of decays.
Deliverables
01
Manage experimentation, training, validation, deployment, and retirement through a governed model registry with versioning and lineage.
02
Automate testing, validation, evaluation, and deployment with ML-specific pipelines that handle data, model, and prompt artefacts together.
03
Track experiments, parameters, metrics, datasets, and artefacts for reproducibility, comparison, and audit.
04
Monitor latency, throughput, quality, hallucination rate, cost, and business metrics in real time with alerting and dashboards.
05
Detect data drift, concept drift, and prompt regression automatically - and trigger retraining or rollback workflows.
06
Per-model and per-call cost observability, model routing for cost-quality balance, caching strategies, and quarterly cost reviews.
01
Audit current ML practices, tools, deployment maturity, and governance gaps - and define the target MLOps / LLMOps reference architecture.
02
Stand up the model registry, experiment tracking, CI/CD pipelines, monitoring, and feature store integration.
03
Migrate priority models into the platform with end-to-end automation, evaluation, monitoring, and drift detection.
04
Run continuous evaluation, cost optimisation, governance reporting, and platform evolution as the use-case portfolio grows.
Bespoke deployment scripts per model break under audit and scale.
Models that pass unit tests but fail evaluation slip into production and erode trust.
Latency dashboards do not catch quality decay. Model and prompt monitoring are separate disciplines.
Prompt versioning, evaluation, and cost-per-call are first-class LLMOps concerns most ML platforms miss.
GenAI cost surprises kill production roll-outs. Per-call cost telemetry is mandatory.
Approval and risk classification have to live in the pipeline, not in a parallel review process.
Deployment frequency 10x baseline within 6 months
Mean time to detect model drift under one hour
100% of production models tracked in governed registry
Evaluation pipelines enforced for every release
Per-model cost dashboards reviewed monthly
Zero ungoverned models in production
MLOps covers the lifecycle of classical ML models - training, deployment, monitoring, retraining. LLMOps extends this with prompt versioning, evaluation frameworks, model routing, hallucination tracking, and cost-per-call observability specific to large language models and GenAI systems.
Yes. Even three or four production models benefit from automated testing, governed deployment, monitoring, and reproducibility. Without it, technical debt compounds fast.
We are platform-agnostic. Common stacks include MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI, Databricks, LangSmith, LangFuse, Arize, and emerging LLMOps platforms - chosen to fit your stack and scale.
A foundational MLOps setup typically takes 6-12 weeks. Full maturity - covering every production model, automated evaluation, and FinOps - is iterative and grows with the portfolio over 12-18 months.
Yes. We extend existing DevOps pipelines with ML-specific stages - data validation, training, evaluation, model registry, and governed deployment - rather than replacing them.
Model documentation, evaluation evidence, approval workflows, monitoring, and audit trail are integrated into the MLOps pipeline so responsible AI is enforced by the platform, not by review meetings.
Prompt versioning and registry, RAG evaluation, hallucination tracking, prompt regression testing, model routing, semantic caching, and per-call cost observability.
Yes. For centers that want to focus on use cases, we can operate the MLOps / LLMOps platform as a managed service with agreed SLAs.
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