Need a breakthrough AI product? We build versions with just enough features to satisfy early users and generate critical feedback for your next iteration.
An MVP — Minimum Viable Product — is the leanest version of your AI product that real users can actually use and give feedback on. It's not a prototype. It's a working product, deliberately scoped.
We help you define exactly which features belong in the MVP and which should wait — then we build it fast, deploy it to real users, and help you capture the insights that will shape the full product roadmap.
Typical MVPs take 8–16 weeks. They're built with production-grade code — not throwaway prototypes — so they can be scaled when you're ready.
MVP alert system for a health ministry — ingesting facility data, running outbreak detection models, and surfacing alerts to district health officers via a web dashboard.
MVP credit assessment tool for a microfinance institution — collecting psychometric and behavioral inputs, scoring applicants, and returning recommendations in real time.
WhatsApp-based MVP for agricultural advisory — answering crop questions, delivering weather alerts, and routing complex queries to human extension officers.
MVP dashboard for an NGO — aggregating KoBoToolbox field data, computing programme indicators automatically, and generating formatted donor reports on schedule.
MVP recommendation widget for an e-commerce platform — collaborative filtering model serving personalised product suggestions on product and cart pages.
MVP forecasting dashboard for a retail chain — time-series models predicting weekly demand per SKU, with inventory alerts and export to procurement systems.
The best MVPs don't try to do everything. They solve one specific, high-value problem better than the current manual process — and prove they can do it reliably.
We build MVPs that actual users — health workers, field officers, analysts — can operate in their real work context, not just in a demo environment.
Every interaction is tracked so you can understand how the product is being used, where the model struggles, and what users actually need — before you build version 2.
We write production-grade code from day one. When your MVP succeeds and it's time to scale, you're extending — not rewriting.
Let's define your MVP scope in a free discovery call — and agree on what success looks like before we write a single line of code.