Quarters lost to discovery theater
Slide decks, workshops, and architecture debates while competitors ship. A sprint replaces the debate with a working product you can put in front of users.
AI MVP Sprint
Last reviewed A working AI product in 14 days. Fixed scope, fixed price.
The AI MVP Sprint takes one well-defined product idea — an LLM application, RAG assistant, agent workflow, or AI-powered tool — from scoping call to deployed, demoable product in 14 days. Senior engineers only, evaluation gates and cost telemetry designed in from day one, and delivery for US, Canada, UK, and EU teams with real timezone overlap.
14 days
Scoping call to deployed product
158 TPS
Measured, published engineering
$0.47/M
Cost engineering built in
Agency quotes that assume a rewrite
Typical US/UK agency AI builds start at six figures and 3-6 months. A fixed-scope sprint proves the product thesis first, for a fraction of that.
Demos that never survive production
Most AI prototypes die when real data, real users, and real costs arrive. Sprint builds include evaluation gates and cost telemetry so the demo is the product.
Deep Dive
Why a fixed sprint beats an open-ended AI engagement
AI product risk is concentrated at the start: is the data good enough, does the model reach usable quality, do unit economics work? An open-ended engagement spreads that discovery across months of billing. A fixed sprint forces the risk questions to be answered in the first two days — and prices the whole thing before you commit.
The sprint inherits everything NavyaAI publishes: measured serving benchmarks, evaluation harnesses, and cost models. That's why cost telemetry and eval gates are in every build — they're not add-ons, they're how we work.
Economical does not mean junior
Sprint teams are senior engineers — the people behind our published benchmarks and production platforms — operating from an India cost base with working-hours overlap for US, Canada, UK, and EU clients. You get US-agency engineering quality at a price that lets you test a product thesis without betting the round on it.
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.
One product goal, defined and frozen on day 1-2 with you
Model and provider selection matched to quality needs and unit economics
RAG, agent, or fine-tune architecture — whichever the problem actually needs
Evaluation harness: golden sets, regression gates, hallucination checks
Cost telemetry: per-request and per-workflow spend visible from the first deploy
Handover: your repo, your cloud, documented, with a 2-week support window
The 14-day sprint plan — see the full map
A fixed cadence with demos throughout — you see the product grow, not a final reveal.
| Days | Phase | What you get |
|---|---|---|
| 1-2 | Scoping & architecture | Frozen scope doc, model/provider choice, data audit, success criteria |
| 3-6 | Core build | Working core loop deployed to staging — first demo |
| 7-10 | Product build | Full flows, UI, integrations — second demo with your team |
| 11-13 | Evals & hardening | Evaluation gates, cost telemetry, load checks, security pass |
| 14 | Handover | Production deploy, docs, repo transfer, support window starts |
How It Works
How the audit works
Step 1
Book the scoping call
30 minutes: the product idea, your data, your constraints. We tell you honestly whether it fits a 14-day window.
Step 2
Scope freeze and fixed quote
Days 1-2 produce a frozen scope document, architecture, and the fixed price. No surprises after this point.
Step 3
Build with demos
Working software from day 6, demos at each phase, your feedback folded in while the sprint runs.
Step 4
Handover on day 14
Production deploy in your cloud, your repo, documentation, eval suite, cost telemetry, and a 2-week support window.
One idea in, one working product out — in 14 days.
Bring the problem and the data you have. The scoping call defines the fixed sprint scope, deliverables, and price 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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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.
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Web design and development agency serving UAE brands — high-volume portfolio of business sites built for speed and search.
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FAQ
Common questions
What can realistically be built in a 14-day AI sprint?
One well-scoped product: a RAG assistant over your documents, an LLM-powered workflow tool, an agent that automates a defined process, or an AI feature inside an existing product. The scoping call is where we confirm the idea fits the window — if it doesn't, we say so and propose a phased plan instead.
What does the AI MVP sprint cost?
The sprint is fixed-price, quoted at the scoping call based on scope — from $9,900. Because the team is senior engineers on an India cost base with US/EU timezone overlap, the price is typically a fraction of an equivalent US or UK agency build.
Who owns the code and IP?
You do, fully. The sprint delivers into your repository and your cloud accounts, with IP assignment in the contract. Nothing is locked to NavyaAI infrastructure.
Do you work with US, Canada, UK, and EU companies?
Yes — most sprint clients are in those regions. We schedule scoping, demos, and standups with real timezone overlap, contracts are GDPR-aware for EU data, and delivery happens in your cloud region.
What happens after the 14 days?
Every sprint includes a 2-week support window. After that, teams either take the product forward themselves (it's their code), continue with our end-to-end product development team, or move into cost-optimization work as usage grows.