A production MVP with AI in the core. Live in six weeks. Fixed price.
Our AI-orchestrated SDLC compresses discovery, build, test and deploy into a six-week programme. Real users on day 42.
What is AI MVP development?
AI MVP development at Eazyware is a six-week, fixed-price programme called Launch 6. Using an AI-orchestrated development lifecycle, senior engineers deliver a production-grade minimum viable product with authentication, one complete user workflow, one production AI capability, analytics, cloud deployment and monitoring, live with real users on day 42.
| Service line | AI Strategy & Discovery |
|---|---|
| Engagement | Fixed-price program (Launch 6) |
| Duration | 6 weeks, fixed |
| Starting price | $26,500 |
| Typical range | $26,500 – $45,500 |
| Deliverables | 6 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Traditional MVPs take four to six months and arrive without AI, or with a bolted-on chatbot. By then the market has moved and the budget is gone.
How do we approach it?
Launch 6 compresses the lifecycle without skipping it. Week one ends with a scope lock: one user segment, one complete workflow, one AI capability, written down and signed. From then on AI handles most of the typing, specification drafts, scaffolding, test generation, boilerplate, and senior engineers spend their time where judgement matters: architecture, business logic, security, the AI capability's evaluation, and the parts of the interface that decide whether users come back. Fridays are demos on staging, and every demo is a chance to cut, never to add. Week five is hardening: load tests, security checks, evals on the AI component, rollback rehearsal. Week six is launch on production with monitoring and a handover pack.
What do clients use it for?
- Launch an AI-native SaaS to first customers
- Ship an internal AI tool to a pilot team
- Replace a manual process with a copilot-driven app
- Investor-ready product in one quarter
Is it the right fit?
Good fit when
- Startups with a validated idea and a launch date
- Enterprises spinning out a new digital product
- Teams that want a fixed price and a fixed date
Probably not when
- Unclear scope that cannot be locked in week one
- Products that need three or more workflows at launch
What do we build?
- Week-one scope lock: one core workflow, one user segment, one AI capability that matters
- AI-assisted specification, scaffolding, test generation and CI/CD from day one
- Senior engineers own architecture, business logic, security and UX
- Weekly Friday demo, production deploy with observability and rollback
What you get
- Auth, roles, admin
- One complete user workflow
- One production AI capability: copilot, extraction, retrieval, agent or recommendation
- Analytics instrumentation
- Cloud deployment, CI/CD, monitoring, evals
- Handover docs and 30 days post-launch support
How does the engagement work?
- 01
Week 1: scope lock and design
- 02
Weeks 2–4: build in sprints
- 03
Week 5: hardening, evals, load
- 04
Week 6: launch and handover
What does good look like?
Real users on day forty-two, on production, with analytics instrumented so week seven decisions are based on usage rather than opinion. Authentication, roles and admin work. The core workflow completes end to end. The AI capability has an evaluation score you can quote. The codebase is one your own team or ours can extend, with CI/CD, infrastructure as code and documentation. And there is a v1.1 backlog, priced, of everything that was deliberately left out.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | 6 weeks, fixed | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Six-week programmes fail when scope drifts or when speed is bought by skipping tests and security. Our answer to the first is the day-five scope lock and a backlog for everything else; our answer to the second is that hardening is a fixed week, not a stretch goal. We also avoid the temptation to make the AI capability the whole product; the workflow has to work without it, so that the AI is a feature users choose, not a dependency that breaks the product when a model changes.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Shipped on the fixed date
- Core workflow completion rate
- AI capability eval score before launch
Which technologies do we use?
- React / Next.js
- Node.js
- MongoDB / Postgres
- React Native
- OpenAI / Anthropic
Who does the work?
A squad of four to five: a product-minded principal, two engineers, an AI engineer and a part-time designer, with a delivery lead keeping the calendar honest.
What do you need to bring?
A decision-maker available every Friday, a clear first user segment, and the discipline to keep week-one scope locked. Access to any system the workflow depends on and an account with your chosen cloud and model providers. Real users ready to try the product in week six.
Frequently asked questions
What if scope grows?
Anything outside the day-five scope lock goes to a v1.1 backlog, priced separately.
Which stack?
React or Next.js, Node.js, MongoDB or Postgres, React Native for mobile. Production-grade and yours to own.
After launch?
Optional Care Plan or feature retainer.
Where does this fit?
AI-Accelerated MVP is part of our AI Strategy & Discovery line. See all pricing or talk to an engineer.