US agency rates burn the raise
Typical US/UK agency AI product builds run $200-400K+ before launch. For a seed or Series A company, that is a material slice of the round spent before revenue.
AI Product Development
Last reviewed End-to-end AI product development without the agency invoice.
NavyaAI builds complete AI products — discovery, design, engineering, deployment, and operations — for startups and enterprises in the US, Canada, UK, and EU. Senior engineers whose benchmarks are published with raw data, an India cost base that changes what your budget buys, full IP assignment, and GDPR-aware contracts for European data.
40-60%
Below typical US/UK agency pricing
158 TPS
Published, measured engineering
100%
IP assignment, every contract
Cheap outsourcing that ships demos, not products
The low-cost tier of outsourcing produces AI demos that collapse under real data, real load, and real bills. The gap is engineering depth, not geography.
AI products with invisible unit economics
Most teams discover their per-request cost after launch — when the bill arrives. We design cost telemetry and routing policy into the product from the first commit.
Deep Dive
The raise math: why build cost is a survival variable
For a funded startup, the real question is not 'what does the build cost' but 'how many product iterations does the runway buy'. At US agency rates, one AI product build can consume 15-25% of a seed round before first revenue. At NavyaAI's cost base, the same round funds the build plus the two or three pivots that usually stand between version one and product-market fit.
That is the actual meaning of 'economical' here: not cheaper engineers, but more attempts at the target for the same capital.
Judge the team by published, measured work
Most development agencies show logos. We publish benchmarks with raw data: LLM serving on a $499 edge board (158 tokens/sec aggregate, failures included), a 70B production stack at $0.47 per million tokens, and inference-optimization audits with before/after invoices. The engineering culture that produces honest public benchmarks is the one that builds your product.
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.
Discovery: product goals, data reality, model feasibility, unit economics
Design: UX, workflows, and architecture shaped around measured model behavior
Build: LLM apps, RAG, agents, fine-tunes, and the serving stack underneath
Evaluation: golden sets, regression gates, and quality dashboards
Deploy: your cloud, your region — US, Canada, UK, or EU — with data residency honored
Operate: monitoring, cost telemetry, model updates, and an SLA that fits your stage
What your budget buys: typical US/UK agency vs NavyaAI — see the full map
Directional comparison for a production AI product build (RAG assistant or agent workflow class).
| Line | Typical US/UK agency | NavyaAI |
|---|---|---|
| Team | Mixed seniority, junior-heavy delivery | Senior engineers only — the team behind our published benchmarks |
| MVP to production | $200K-$400K+, 3-6 months | From $24,000, phased, working software from week 2 |
| Ongoing team | $50K-$80K/month | From $11,500/month with timezone overlap |
| Cost engineering | Rarely included | Evals + cost telemetry in every build, by default |
| IP & code | Varies by contract | Full IP assignment, your repos, your cloud — always |
How It Works
How the audit works
Step 1
Discovery call
30 minutes on goals, data, constraints, and budget. You get an honest read on feasibility and a proposed shape for the engagement.
Step 2
Feasibility and plan
A short paid discovery (or straight to sprint for clear scopes): data audit, architecture, phased plan, and a concrete quote.
Step 3
Phased build
Working software from week 2, demos every phase, evals and cost telemetry from the first deploy.
Step 4
Operate or hand over
Run it with our team under an SLA, or take full ownership — your code either way.
The build your roadmap needs, at a price your runway allows.
A 30-minute discovery call maps your product goals to a concrete plan: team shape, phases, timeline, and monthly cost — before any commitment.
$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 auditShipped Work
Output that shipped and scaled.
Products we've engineered and clients we've served — real platforms in production, not slideware.
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FAQ
Common questions
What does end-to-end AI product development include?
Everything from idea to operated product: discovery and feasibility, product design, LLM/RAG/agent engineering, the serving infrastructure underneath, evaluation harnesses, deployment into your cloud, and ongoing operations with cost telemetry. You can enter at any phase — many clients start with our 14-day MVP sprint and grow into a full product team.
How much does AI product development cost with NavyaAI?
Phased builds start from $24,000 and ongoing product teams from $11,500/month — typically 40-60% below equivalent US/UK agency pricing, because our senior engineers work from an India cost base with US/EU timezone overlap. Every engagement is quoted concretely at the discovery call; there is no rate-card ambiguity.
How do you work with teams in the US, Canada, London, and the EU?
Deliberate timezone overlap for standups and demos, communication in your tools (Slack, Linear, GitHub), contracts under mutually agreed jurisdiction with full IP assignment, and deployment in your cloud region. For EU clients we work GDPR-aware by default: EU data residency, processor agreements, and no training on your data.
Who owns the product, code, and models?
You do — completely. Code lives in your repositories, infrastructure in your cloud accounts, and fine-tuned model artifacts belong to you. IP assignment is standard in every contract.
Why is NavyaAI more economical than a US or UK agency?
Cost base, not quality. The same senior engineers who publish measured benchmarks — 158 tokens/sec on a $499 edge board, $0.47 per million tokens on an optimized 70B stack — build client products from India, where senior engineering costs a fraction of San Francisco or London rates. The savings are structural, and they show up in your quote.
Can you take over or extend an existing AI codebase?
Yes. Roughly half of product engagements start from an existing codebase — an agency build that stalled, an internal prototype that needs hardening, or a product whose AI costs got away from the team. We audit first, then extend.