NavyaAI logoNavyaAI

Production AI Engineering

Build AI that survives production. See the cost, progress, and proof.

NavyaAI builds agents, RAG systems, private LLMs, and AI infrastructure with evals, budgets, observability, and transparent delivery built in from day one. Daily written progress on every engagement — tasks, commits, blockers, and spend. US, Canada, UK, EU & Australia.

Sprint from $5,500 · Production builds from $24,000 · Audit is free, no call required.

Isometric illustration of an AI infrastructure stack, from silicon die to GPU boards to server racks to cloud nodes

Trusted by teams behind

CoreDiroNeoPPC RoyBuildUNIX

Why teams pick us

Nothing hidden.

Don't take our word for it. Inspect the price, the progress, and the proof.

Price you can inspect

Know what the team and the delivery layer cost — itemized on every invoice, with a public calculator.

Check the math

Progress you can see

Daily written progress on every engagement: tasks, commits, blockers, scope changes, and spend stay visible. Calls only when useful.

How we work

Proof you can reproduce

Evals, benchmarks, methodology — with raw CSVs and the failures included. Judge the team by data, not a portfolio page.

Read the reports

What we do

Build it. Optimize it. Bring it in-house.

The demo is the easy 80% — every lane ships the production 20% too.

01

Build

AI products that survive production

One idea to a deployed, evaluated product — agents, RAG, LLM apps — with the production 20% engineered in from day one.

  • 14-day fixed-scope MVP sprint — working product, not a deck
  • Daily written progress: tasks, commits, blockers, and spend
  • Evals and cost telemetry in every build, full IP transfer

Measured

Ship
day 14
Software
from week 2
Ledger
daily

Sprint from $5,500 · Builds from $24,000

Get a build plan
02

Optimize

AI bills, stopped leaking

A written map of where spend inflates — agent loops, retries, routing, idle capacity — before you buy more.

  • Free written inference audit for $20K+/month spenders
  • Cost per completed action, not cost per token
  • Measured before/after, methods published with the data

Measured

Case
$47K→$28K
Cut
42%
Throughput
2.3×

Audit is free · No call required

Start the free audit
03

Deploy Private

AI inside your perimeter

Private LLM and RAG stacks in your facility and jurisdiction — sized from benchmarks we ran, not vendor claims.

  • On-prem & colocation stacks, edge boards to H100 pairs
  • GDPR and EU data residency by architecture, not by clause
  • Break-even math before any hardware is bought

Measured

Measured
158 TPS
Board
$499
Serving
$0.47/M

Consult free · TCO before hardware

Book a deployment consult

The HPC difference

Engineered from the transistor up.

Most AI agencies start at the API call. We start at the silicon — electrical & computer engineering roots, HPC research lineage, and hardware-aware serving that squeezes measured performance out of every layer between the die and your product.

TransistorKernelGPUClusterCloud

Benchmarks with raw data

We publish throughput, power, and cost measurements with unedited CSVs — including the configurations that failed. Judge the engineering by data, not a portfolio page.

Jetson edge benchmark report

Edge to HPC, one ladder

8-watt Jetson boards serving 16 concurrent users at ~$14/month, up through L40S nodes to H100 pairs serving 70B models at $0.47 per million tokens. Sized by measurement, not vendor sheets.

GPU requirements: edge to HPC

Cost telemetry by default

Every system ships knowing its own unit economics — per-request and per-workflow spend visible from the first deploy. The 42% cheaper inference playbook is public.

The Token Tax benchmarks

Shipped Work

The proof is shipped.

No borrowed logos, no stock quotes — products we engineered, live in production for our clients and ventures.

BuildUNIX — product preview
Construction SaaS

BuildUNIX

Construction execution platform for PMC firms — phase-gated workflows, tamper-proof site records, and snag-to-handover tracking that replaces spreadsheets and WhatsApp threads.

Full-stack platform: Next.js, Postgres, async job pipeline, field-team mobile flows.

VectraGPT — product preview
Enterprise AI SaaS

VectraGPT

Secure AI chatbots for HR, support, and sales — RAG-powered answers from company documents with audit trails, aligned to GDPR and SOC 2 expectations.

RAG platform end to end: ingestion, retrieval, guardrails, multi-tenant serving.

XeoRank — product preview
Developer SaaS

XeoRank

SEO, AEO & GEO intelligence engine for developers — triple scoring, Search Console integration, and CLI-first plus MCP agent workflows.

Agent-first product: MCP server, site crawler, scoring engines, WordPress control plane.

AdFargo — product preview
AI Creative SaaS

AdFargo

AI creative strategist — learns a brand from its URL and generates on-brand ads, video, and copy built to convert, in minutes.

Generative pipeline: brand ingestion to multi-format creative output.

Rankgent — product preview
SEO SaaS

Rankgent

Programmatic SEO platform — generate, preview, and publish thousands of service + location pages in minutes. Trusted by 500+ SEO professionals.

Page-generation engine: templating, bulk preview, and publishing at 50K+ pages scale.

Cheval — product preview
Digital Agency · Dubai

Cheval

Web design and development agency serving UAE brands — high-volume portfolio of business sites built for speed and search.

Engineering partner: platform performance and search infrastructure.

Also trusted by

Core
Diro
Neo
PPC Roy
Scale Minds
WinWin
DMS
Digital Crats

How we work

Speed is a process, not a promise.

  1. Step 1

    Scope in days, not months

    A 30-minute call, then a frozen scope, architecture, and fixed quote within 48 hours. Honest feasibility first — if an idea doesn't fit the window, we say so.

  2. Step 2

    Build with demos

    Working software from week 2 at the latest. Demos at every phase, your feedback folded in while the build runs — no final-reveal surprises.

  3. Step 3

    Evals & cost telemetry, by default

    Golden sets, regression gates, and per-request cost visibility ship inside every build. The demo that survives production is the one that was measured.

  4. Step 4

    Handover or operate

    Your repos, your cloud, full IP assignment — take it over with runbooks and training, or keep us on under an SLA that fits your stage.

Ramachandra Vikas Chamarthi, Founder of NavyaAI

Ramachandra Vikas Chamarthi

Founder, NavyaAI

LinkedIn

Who you work with

“Autonomy without control becomes risk. Intelligence without governance becomes liability.”

Vikas is a systems-first AI technologist specializing in high-performance agentic infrastructure, developer tooling, and scalable AI platforms. With a Master’s in Electrical & Computer Engineering from UNC Charlotte and HPC research roots, he designs AI systems that are secure, efficient, and production-ready from day one — control planes for agents, hardware-aware serving, and capital-efficient execution.

TransistorKernelGPUClusterCloud

Track record

Head of AI/MLHeyNeoUSHead of MLOpsCode and Theory · Stagwell GroupUSResearchTeCSAR Lab, UNC CharlotteUS

Founder experience — US roles & engagements at

Code and Theory (Stagwell Group)ProsciaWolframHeyNeoTeCSAR Lab · UNC Charlotte

M.S. Electrical & Computer Engineering, UNC Charlotte · 98% export revenue mix · every intake read personally

Already running AI?

Find the leak before you buy more capacity.

Teams spending $20K+/month on OpenAI, Azure OpenAI, Bedrock, RAG, agents, or self-hosted LLMs get a free written leak map — where the bill inflates and what to change first. 20 seconds to start, no call required.

Cloud bill leaking instead? Free egress audit

What the leak map covers

  • Agent loops and retries multiplying token volume
  • Prompt and context bloat on every request
  • Frontier models serving small-model work
  • Idle or overprovisioned GPU capacity
  • RAG, vector-store, and egress overhead

$47K → $28K

Monthly bill after one audit — published case study, measured before and after.

Read the case study

Engineering blog

We publish what we measure.

All posts

FAQ

Common questions

Still unsure which path fits? Thirty minutes on a call maps your situation to a concrete plan with a price on it.

Book a call instead
How fast can NavyaAI build an AI product?

A working, deployed MVP in 14 days through the fixed-scope AI MVP Sprint (from $5,500): scope frozen on days 1-2, working software by day 6, evaluation gates and cost telemetry by day 13, handover on day 14. Larger products run as phased builds with working software from week 2.

What does AI product development cost with NavyaAI?

The 14-day MVP sprint is fixed-price from $5,500, and phased production builds start from $24,000. Ongoing teams run as senior AI pods with fully transparent per-role pricing — role compensation plus a flat, itemized 20% delivery layer, with the complete math in a public calculator. Full IP assignment in every contract.

How does the free AI inference audit work?

Teams spending $20K+/month on LLM APIs or GPUs share their spend range, provider/workload, and stack in a 3-minute intake. NavyaAI replies with a written leak map — where the bill inflates across agent loops, retries, routing, and idle capacity, and what to change first. No call required; our published case cut one bill from $47K to $28K per month.

Can NavyaAI deploy AI on-premise for GDPR compliance?

Yes — we design, deploy, and operate private LLM and RAG stacks in your data center or your chosen colocation facility, so prompts, documents, and embeddings never leave your jurisdiction. EU data residency holds by architecture rather than contract clauses, and every tier is sized from our published benchmarks, from 8-watt edge boards to H100 clusters.

How do I know what's happening during an engagement?

Every NavyaAI engagement runs with automatic AI progress tracking: an AI agent watches the repositories and task boards and sends you a daily plain-English report — what shipped, what's in flight, what's blocked, and spend against budget. Every task, commit, blocker, scope change, and spend variance stays visible, and calls happen when they're useful rather than as status theater. Combined with itemized invoices and benchmarks published with raw data, nothing about the engagement is hidden.

Which regions does NavyaAI work with?

Most clients are in the US, Canada, UK, and EU. Engagements run with deliberate timezone overlap for standups and demos, communication in your tools, contracts under mutually agreed jurisdiction with full IP assignment, and deployment in your cloud region — GDPR-aware by default for EU data.

What makes NavyaAI different from other AI agencies?

Hardware-up engineering and published proof. The team spans transistor-level architecture to cloud-scale inference, and publishes benchmarks with raw data — 158 tokens/sec on a $499 edge board, $0.47 per million tokens on an optimized 70B stack — including the configurations that failed. Every build ships with evaluation gates and cost telemetry.

Start here

One call. Three ways forward.

Thirty minutes to map your goal — build, optimize, or deploy private — to a concrete plan with a price on it. Or skip the call entirely and start with the free written audit.