AI-Native MVP package
USD $30K–$70KTypical investment
2–3 monthsFrom idea to launch
What teams come to us for
Launch to market quickly
A working product in front of real users in 2–3 months.
Test before scaling
Check that a product, service, or feature works before a bigger build.
Replace manual work
Turn slow, repetitive processes into automated workflows.
We scope tightly, build fast, and deliver end to end
Get an estimate
What’s included
- Focused MVP scope
- Working product, ready to scale
- CI/CD deployment
- Usability testing and QA
- Post-launch support
- Handover documents
Next steps after launch
How we get you there
Prioritize
Pick the features that matter most for the first release.
Build
Turn the prioritized scope into a working product.
Validate
Test usability and product quality before real-world use.
Launch and stabilize
Go live and fix issues fast.
What AI-native means for you
In how we build
0%
faster sprint delivery
AI agents and copilots work in every sprint, drafting specs, code, and tests for our engineers to review.
0%
lower delivery cost
Compared with a traditional build of the same scope.
Inside your product
AI features that do real work
Not just a chatbot. Add them from version one, or later as you grow.
- Smart search
- Automated workflows
- Personalization
- Data and intelligent systems
How fast an AI-native MVP can be
9:41
4 min
Transportation
White-label shuttle booking MVP launched in 1.5 weeks
- 1.5 weeksFrom idea to a market-ready MVP
- 2–3×Faster than a standard sprint cycle
The challenge. Launch a new white-label shuttle booking platform without diverting engineering. Comparable launches took 3–5 weeks.
How we did it. An AI agent embedded in the dev workflow, working alongside the team through ideation, spec drafting, and build, so product and engineering handoffs became one continuous flow.
1,284
86%
+24%
Healthcare
Multi-tenant superadmin shipped in 6 weeks
- 6 weeksFrom single-tenant to a multi-tenant platform
- 100%Scope delivered, including mid-build requests
The challenge. A single-tenant healthcare admin had to become a multi-tenant superadmin with cross-clinic login, across several repos, while the client added feature requests mid-build.
How we did it. An AI spec-driven pipeline. Developers set the architecture, plan, and gitflow first. A unified tenant model then drives AI-generated code across all repositories, with BAs and QAs tracking specs through MCP.
More MVPs we’ve built
PROJECTOWNERDUESTATUS
Education · Canada
Workforce and operations ERP
A centralized ERP connecting workforce management, training, sales, field operations, and compensation in one flow.
Web appSales and CRMField operations
Compliance · Australia
Compliance management platform
One place to structure and manage compliance documents, so internal teams and consultants work from the same workflow.
Web appDocument intakeRequirement mapping
1,284
86%
+24%
PropTech · Vietnam
Property operations and intelligence platform
Rental operations, booking, leasing, finance, and portfolio insight for managers, investors, and tenants.
Web and mobileBooking portalAI automation
Common questions
What can be built in 2–3 months?
The main user journey, sign-up, real data, and one or two key features.
Do we own the code?
Yes, from day one. Code, documents, and cloud accounts are yours.
Why not a no-code MVP?
No-code limits custom features and integrations. Real code can grow with you.
Is AI-built code safe?
Every change is tested and approved by a senior engineer.
Can you continue after launch?
Yes. We keep building with you, or hand over to your team.
Accelerate time-to-market with an AI-Native MVP
Share your ideas with us. We'll get back to you with the right next step.
or write to services@enosta.com