MVP Development
Validate the idea before you commit the budget. We build the smallest version of your product that can survive contact with real users, get it in front of them fast, and iterate on what the data actually says, instead of what the roadmap assumed.
What's included
- Scope-first planning: identifying the riskiest assumption and building only what tests it
- A production-grade MVP, not a disposable prototype that has to be rebuilt to scale
- Fast iteration cycles once the product is in front of real users
- A clear path from MVP to the next stage of the product, with or without Omnihash
How we approach the work
Choose the assumption to test
Decide what must be learned from real users before adding a feature list. A disposable prototype can settle a design question; the MVP should test the business-critical workflow.
Build the smallest real slice
Keep scope narrow, but include the authentication, data, deployment, and reliability needed for a real user to complete the task. That makes the first release useful rather than just presentable.
Use evidence for the next decision
Put the product in front of users, observe where they succeed or get stuck, and use that feedback to decide what to change, expand, or stop.
Who this is for
- Founders who need a fundable, demo-ready product fast
- Teams validating a new feature or market before committing a full roadmap to it
- Companies with an idea and a deadline, not yet a full spec
Related work
- A production AI agent platform, zero to live in a month — A custom, production-grade AI agent platform built from scratch and shipped to production in one month — after a previous team spent 8+ months and shipped nothing.
Related engineering notes
- How much does it cost to build an AI MVP? — What an AI MVP actually costs, and what drives the budget: scope, integration, and data, not the model. A scope-first framework to ship in weeks.
- Throwaway HTML: the fastest way to kill a bad product idea — Why disposable single-file HTML prototypes beat slide decks and design files for early product decisions, how AI speeds them up, and when to stop.
- Why most AI pilots never ship — Most AI pilots produce a great demo and quietly die at the production gate. The reasons they stall, and how to scope a pilot that actually ships.
Want to talk through your project?
Send a couple of sentences about what you're building. We'll reply with how we'd approach it.
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