AI MVP Development
A working, deployed AI product in a fixed 45-day scope.
We build full-stack AI-native MVPs: web and mobile products built around one or two genuinely valuable AI features, taken from idea to a deployed, tested, and documented product. Our flagship engagement model is a fixed-scope, fixed-price 45-day build: a 20-minute scoping call and a 2-page proposal define the scope, weekly Friday demos show real progress instead of a black box, and launch means the product is deployed, tested, documented, and properly handed over. We've shipped MVPs across e-commerce (DesignT), legal tech (Legal Assistant), aviation training (FlightDeck), ML tooling (OptimaFlow), fintech (Kanaka Gold Loan), and health tech (Health Activity Dashboard), each built around a small number of AI features that justify the product, not AI bolted onto everything.
Who this is suitable for
- Founders or teams that need to validate a product idea with a real, working build, not a slide deck
- Businesses that know the AI feature they want but need the surrounding product (auth, data, UI, deployment) built around it
- Teams that want weekly visibility into progress instead of a black-box delivery
Business problems this addresses
- An idea that needs to become a real, testable product before committing to a larger build
- AI feature ideas with no surrounding product to put them in front of users
- Previous development engagements with unclear scope, silent progress, or missed handover
What we actually deliver
- A deployed, working web and/or mobile product
- One or two AI features that are the actual reason the product is valuable, not decoration
- Auth, payments (where relevant), and core data flows built around the AI feature
- Weekly Friday demos throughout the build
- Full handover: deployed, tested, documented
What's outside the standard scope
- Open-ended feature scope: the fixed-price model requires a defined scope after the initial scoping call
- Ongoing maintenance after launch (covered separately by the Growth Retainer engagement)
- Products with no clear AI feature: that's standard custom software (see Custom Business Software)
Technical capabilities
- Full-stack web (Next.js, React) and mobile (React Native, Expo) product builds
- One or two focused AI features per MVP: conversational design (DesignT/Gemini Vision), document intelligence (Legal Assistant), visual workflow builders (OptimaFlow)
- Cloud deployment on managed infrastructure (Google Cloud Run) with Dockerized, multi-stage builds from day one
- CI/CD pipelines so releases are repeatable, not manual
Integrations and technologies
Delivery process
Every engagement follows the same fixed-scope process: Scope → Build → Launch → Grow. A 20-minute call and a 2-page proposal define fixed scope and price, weekly Friday demos show real progress, and launch means deployed, tested, documented, and handed over.
What affects the timeline
- Number and complexity of the AI features being built around
- Whether the product needs web only, mobile only, or both
- Payment, auth, or third-party integration requirements
- 45 days is the standard Core MVP timeline; smaller validations fit the 10-day Launch Sprint
What affects pricing
- Launch Sprint, starting from ₹60k (~$900), 10 days: landing page, waitlist, analytics, one AI feature demo. Validate before you build
- Core MVP, starting from ₹2.5L (~$3k), 45 days: full web + mobile app, auth, payments, deploy, 1-2 AI features, weekly demos
- Growth Retainer, starting from ₹30k/month (~$450/month): iterations, fixes, monitoring after launch
- Fixed price after a 20-minute scoping call; 50% advance to begin; final quote depends on scope, integrations, AI complexity, and deployment requirements
Security, privacy, and human review
- Cloud Deployment: deployed on managed cloud infrastructure, not a laptop demo or local script
- CI/CD Pipelines: automated build, test, and deploy so releases are repeatable
- Monitoring & Logging: request tracing, error logging, and audit trails from launch
- Data Privacy: client data scoped and access-controlled from day one
See the full list of engineering practices on Built for Production.
Relevant case studies
DesignT
AI-assisted custom t-shirt studio that turns prompts and reference images into product-ready artwork and mockups.
Legal Assistant
Platform for legal professionals with document analysis, legal research, case discovery, OCR, translation, comparison, and AI chat.
OptimaFlow
Visual machine-learning workflow builder focused on training, inference, and quantization experiment design.
Health Activity Dashboard
Cross-platform health dashboard reading Apple Health and Google Health Connect data, with graceful demo-mode fallback and backend sync.
Apex
Mobile-first sports coaching and readiness platform for athletics academies.
Nutrition
AI-assisted weight management application designed around adherence, recovery, and sustainable behavior change.
Common questions
What exactly do I get after 45 days?
A deployed, tested, and documented product with the AI feature(s) working end to end, not a prototype or a demo environment. Handover includes the deployment, documentation, and a working system, following the same process we've used across projects like DesignT, Legal Assistant, and FlightDeck.
What if I'm not sure the idea is worth a full MVP yet?
That's what the 10-day Launch Sprint is for: a landing page, waitlist, analytics, and one AI feature demo to validate interest before committing to the 45-day build.
How is scope decided?
A 20-minute scoping call, followed by a 2-page proposal with fixed scope and fixed price. 50% advance to begin. The final quote depends on scope, integrations, AI complexity, and deployment requirements.
What happens after launch?
Launch includes deployment, testing, documentation, and handover. If you want ongoing iteration, fixes, and monitoring after that, the Growth Retainer (from ₹30k/month) covers it.
Do you build the whole product or just the AI part?
The whole product: auth, payments where relevant, core data flows, and the AI feature(s) that make it valuable. The AI feature is the reason the product exists, not something added afterward.
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