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, automatic AI progress tracking reporting every day's movement to you, and delivery for US, Canada, UK, and EU teams with real timezone overlap.
Need a full product team?- 14 days
- from your first call to your product live
- Fixed sprint window with demos throughout — not a discovery phase that becomes a quarter.
- Daily
- written progress you can read: tasks, commits, blockers and spend
- You see the sprint move every day without a status call, and you can stop it the day it stops making sense.
- $0.47/M
- per million tokens: the cost ceiling we build to for you
- Measured optimized serving cost from our 70B benchmark — sprints inherit cost telemetry from day one. See the data
What goes wrong
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.
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.
How this plays out in practice
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.
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.
- 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
- Automatic AI progress tracking: a daily plain-English report on tasks, commits, and blockers
- Evaluation harness and cost telemetry: quality and spend visible from the first deploy
- Handover: your repo, your cloud, documented, with a 2-week support window
The 14-day sprint plan
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 the audit works
Book the scoping call
30 minutes: the product idea, your data, your constraints. We tell you honestly whether it fits a 14-day window.
Scope freeze and fixed quote
Days 1-2 produce a frozen scope document, architecture, and the fixed price. No surprises after this point.
Build with demos
Working software from day 6, demos at each phase, your feedback folded in while the sprint runs.
Handover on day 14
Production deploy in your cloud, your repo, documentation, eval suite, cost telemetry, and a 2-week support window.
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 auditOne 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.
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 $5,500 ≈ €4,800 / £4,100 / A$7,900. 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.
