AI changes ship without eval gates
Prompt, model, retrieval, and data changes can degrade quality or cost without a release system.
MLOps and AI DevOps
MLOps consulting for AI systems that need to keep running.
NavyaAI helps teams build the operational layer for production AI: model and prompt release flows, eval gates, observability, incident response, infrastructure automation, and cost monitoring across LLM and ML systems.
Case signal
42% cost reduction
Throughput
2.3x improvement
Budget fit
$20K+ monthly AI spend
Incidents lack useful signals
Latency, token spend, hallucination risk, retries, and provider failures need first-class telemetry.
Infrastructure scales before it is measured
GPU and API spend grows faster when utilization and cost per workflow are not tracked.
Audit Focus
What we inspect before prescribing a platform change.
The first pass is designed to identify the smallest useful intervention: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
Model, prompt, and retrieval release workflow
Eval gates before production rollout
Observability for latency, cost, retries, and quality
AI infra CI/CD and deployment automation
Cost and capacity monitoring for APIs and GPUs
Decision Map
MLOps operating map
Production AI needs release controls and cost telemetry, not only model code.
| Layer | Common failure | Audit question |
|---|---|---|
| Release | Prompt/model changes ship manually | What blocks a bad rollout? |
| Evaluation | Tests do not match real workflows | Which cases define quality? |
| Observability | Only provider errors are monitored | Can you see cost per workflow? |
| Infrastructure | GPU/API capacity is overprovisioned | What is current utilization? |
| Governance | No owner for model behavior | Who approves risk changes? |
Qualified Intake
Start with spend, provider, and workload shape.
The audit form routes teams below $20K/month toward self-serve estimators and routes qualified spend into follow-up.
FAQ
Common questions
What is MLOps consulting?
MLOps consulting helps teams design the systems that deploy, monitor, evaluate, and operate ML and LLM workloads in production.
Does MLOps apply to LLM and RAG systems?
Yes. LLM, RAG, and agent systems need eval gates, prompt and model release controls, retrieval monitoring, cost telemetry, and incident response.
Can MLOps reduce AI infrastructure cost?
MLOps can reduce cost by exposing utilization, retries, routing mistakes, prompt growth, and deployment patterns that waste GPU or API spend.