AI prototypes become expensive systems
The first working demo rarely has the routing, monitoring, eval, and cost controls needed for scale.
AI/ML Consulting Services
AI infrastructure consulting for production teams.
NavyaAI helps CTOs, founders, and ML platform teams plan and improve production AI systems: LLM applications, RAG, agents, inference serving, MLOps, observability, cost controls, and private deployment decisions.
Case signal
42% cost reduction
Throughput
2.3x improvement
Budget fit
$20K+ monthly AI spend
Teams need architecture judgment
Provider APIs, open models, RAG, agents, and GPUs each create different operating risks.
MLOps and product decisions collide
Latency, privacy, quality, and cost need one operating model instead of separate vendor choices.
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.
AI application architecture and model route selection
RAG, agent, and retrieval design review
LLM serving cost and reliability risks
Evaluation, observability, and deployment plan
Build vs buy and vendor lock-in decisions
Decision Map
Consulting engagement map
We focus on decisions that affect production cost, reliability, and delivery risk.
| Area | Risk | First question |
|---|---|---|
| LLM apps | No routing or eval policy | Which tasks need which model? |
| RAG | Retrieval quality hides model waste | Which chunks actually answer users? |
| Agents | Loops and tools inflate spend | What is the stop condition? |
| MLOps | No release path for models/prompts | How are changes evaluated? |
| Infrastructure | Capacity bought before measurement | What is the cost per workflow? |
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 does AI infrastructure consulting include?
AI infrastructure consulting includes architecture review, model route selection, RAG and agent design, deployment planning, observability, cost controls, and production reliability planning.
Is NavyaAI an AI development company or a consulting team?
NavyaAI does both. We advise on architecture and cost decisions, then help implement production AI systems when the engagement needs engineering delivery.
Who is a good fit for NavyaAI consulting?
The best fit is a team with production AI usage, meaningful monthly AI spend, or a near-term decision about APIs, RAG, agents, GPUs, MLOps, or private deployment.