Applied AI Development · Last reviewed
Applied AI development that ships with its own cost controls.
NavyaAI builds production AI systems — LLM applications, RAG pipelines, agents, and the serving infrastructure under them — with evaluation gates, cost telemetry, and routing policy designed in rather than retrofitted after the first painful invoice.
See AI Consulting- 42%
- lower serving bill for one client, one engagement
- The optimization playbook our builds inherit: $47K to $28K per month on Llama 3 70B. See the data
- 50-500x
- more tokens per task once your agents loop
- Why agent loops ship with budgets and stop conditions here — unbounded agents multiply spend per task. See the data
- 72%
- of your AI spend sits outside the model call
- The reason cost telemetry ships with the first deploy instead of after the first bad invoice. See the data
What goes wrong
Demos ship, systems don't
The prototype works; the production version needs routing, evals, observability, retries with budgets, and a cost model — the part most builds skip.
Costs are designed in at the start
Model choice, context strategy, and agent loop design set the unit economics before the first user arrives. Retrofitting is 10x harder.
Quality needs a definition
Without golden sets and eval gates, every prompt edit is a production gamble — and every cost optimization looks too risky to ship.
How this plays out in practice
Production AI is an economics problem wearing an engineering costume
The gap between an AI demo and an AI product is rarely model quality — it is everything around the model: what happens on timeout, which traffic gets the expensive model, how retrieval is budgeted, when an agent stops trying, and whether anyone can see cost per user action. Those decisions set unit economics for the life of the system.
We build them in from the first commit: routing policy by task class, context and output budgets, retry classification, agent loop caps, and telemetry that reports cost per completed action from the first deploy. Our cost report found 72% of production AI spend living outside the model invoice — the build either accounts for that or discovers it later on the bill.
Evaluation infrastructure is what makes everything else safe
Every cost optimization and every model upgrade is gated by the same question: did quality hold? Builds ship with golden sets and eval gates so that question has a measured answer. This is the discipline that let our benchmark work ship INT8 quantization with confidence — the 1.3 MMLU point cost was measured, bounded, and accepted deliberately.
The same harness keeps working after handover: prompt edits, model swaps, and retrieval changes run against the gates before production, so the system's economics and quality stay legible to the team that owns it.
What we check first
The first pass looks for the smallest change that moves the bill: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
- Architecture and model routing designed for cost per action
- RAG and retrieval pipelines with measured overhead
- Agent loops with stop conditions and spend budgets
- Evaluation harness and golden sets from day one
- Serving and deployment path: API, private, or hybrid
Build engagement map
What we build and the engineering discipline attached to each layer.
| Layer | What ships | Cost discipline built in |
|---|---|---|
| LLM application | Routed multi-model backend with fallbacks | Cost per action telemetry from first deploy |
| RAG pipeline | Retrieval, reranking, and context budgets | Measured tokens per answered query |
| Agents | Tool use with loop budgets and stop conditions | Per-task spend caps and retry classification |
| Evaluation | Golden sets and release gates | Cost regressions block rollout like quality ones |
| Serving | API, private (vLLM), or hybrid deployment | Break-even math before infrastructure spend |
How the audit works
Share spend and workload shape
Submit monthly spend range, provider mix, token volume, and what your AI product actually do: chat, RAG, agents, extraction, or batch jobs. Takes minutes, no production access needed.
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.
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.
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.
A measured example
$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 auditStart with your name and work email.
Our AI/ML, HPC and MLOps engineers investigate and reply in writing with a verdict on where to look first. No call required.
Common questions
What is applied AI development?
Applied AI development builds AI capabilities into production software: LLM applications, RAG systems, agents, and ML pipelines — engineered for reliability, evaluation, and cost rather than demo performance.
What does NavyaAI build?
Production LLM applications, RAG pipelines, agent systems, inference serving (including vLLM-based private deployments), and the MLOps layer around them: evaluation, observability, release gates, and cost telemetry.
How is cost handled during development?
As an architecture input, not an afterthought: model routing by task class, context and output budgets, agent stop conditions, and cost-per-action telemetry ship with the first version. Our benchmark work — like the 42% serving cost cut on Llama 3 70B — informs the serving choices.
Does NavyaAI work with existing codebases?
Yes. Many engagements harden an existing AI feature: adding evals, routing, telemetry, and serving optimization to a system already in production. The free inference audit is the usual starting point — it shows where the existing build leaks.
How do engagements start?
With the free AI inference audit intake: spend range, stack, and what you are building. Build engagements are scoped from those findings, so the proposal targets measured problems instead of assumed ones.