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
Last reviewed 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.
$47K → $28K
monthly serving bill, one engagement
2.3x
throughput on half the hardware
72%
of AI spend hides outside inference
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.
Deep Dive
Why audits come before re-architecture
Most AI consulting engagements start with a proposed solution — migrate providers, buy GPUs, adopt a framework — and work backwards to justify it. We run the sequence the other way: measure where cost and risk actually concentrate, then size the smallest intervention that moves the number.
The reason is empirical. In our audits the expensive problem is rarely where the team expects it: a routing policy nobody wrote, retrieval stuffing context into every call, retry storms hidden behind a provider SDK, or GPUs running at a fraction of utilization. The Llama 3 70B case study is typical — no migration, no new vendor, a 42% cost cut from tuning what was already there.
Build versus buy, with the numbers attached
The recurring strategic question is whether to keep paying API margins, optimize in place, or bring serving private. Each answer is right for some traffic shape, and the consulting work is making your traffic shape visible: token volume and growth, latency and quality floors, privacy constraints, and the engineering capacity available to operate infrastructure.
We bring benchmark data to that decision — optimized 70B-class self-hosting measured at roughly $0.47 per million tokens against $1.80-$2.50 for frontier APIs — and the operations-cost honesty that most break-even spreadsheets omit. Sometimes the recommendation is to not build: low or volatile volume keeps APIs the right answer, and the engagement says so in writing.
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
Consulting engagement map — see the full 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? |
Worked Example
What an engagement finding looks like
Findings from the Llama 3 70B audit engagement — each one a measured leak, the lever applied, and the result.
| Finding | Lever applied | Measured result |
|---|---|---|
| FP16 weights on 4 GPUs at ~30% utilization | INT8 (GPTQ) quantization | 45% less VRAM, model fits on half the GPUs |
| Default KV-cache eating batch headroom | KV-cache pruning (H2O, 50% budget) | ~30% cache memory freed for batching |
| Low throughput per GPU-dollar | Continuous batching + serving tuning | 2.3x throughput, $0.82 → $0.47 per M tokens |
| No cost telemetry per workflow | Token and utilization instrumentation | Cost per action visible; regressions now gated |
Full methodology and benchmark data published in the Token Tax post and the Llama 3 70B case study.
How It Works
How the audit works
Step 1
Share spend and workload shape
Submit monthly spend range, provider mix, token volume, and what your AI systems actually do: chat, RAG, agents, extraction, or batch jobs. Takes minutes, no production access needed.
Step 2
Map cost to workflows
We break the invoice into cost per completed user action: prompt and output tokens, retries, retrieval, tool calls, and orchestration overhead — the 72% of AI cost that hides outside the model call.
Step 3
Rank levers by friction and savings
Each leak gets a lever — routing, caching, prompt compression, retry control, quantization, or a private break-even case — ranked by expected savings against implementation effort.
Step 4
Get the written read, then decide
You receive the first cost-leak read in writing. If the economics justify deeper work, the next step is a scoped engagement; if not, you keep the findings.
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.
$47K → $28K
Case study: a Llama 3 70B production workload moved from 4 GPUs to 2 with INT8 quantization, KV-cache pruning, and serving changes — a 42% monthly cost cut with 2.3x throughput.
Read the full auditFAQ
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.
How much does AI consulting cost?
The entry point is free: the AI inference audit intake produces a written first read on cost and architecture leaks at no charge. Paid engagements are scoped after that, sized to the decision at hand — an architecture review prices very differently from a deployment build-out — so you see the findings before committing budget.
What does an AI infrastructure audit deliver?
A written map of where spend and risk concentrate: cost per workflow, model routing opportunities, RAG and agent overhead, serving utilization, and — where volume justifies it — self-host break-even math. A recent audit of this type cut a Llama 3 70B serving bill 42%, from $47K to $28K per month.
How long does an AI infrastructure engagement take?
The free audit read arrives within days of intake. Scoped engagements typically run from a focused architecture review measured in weeks to ongoing advisory across a quarter — the audit findings determine which shape fits.