SMEs & startups Usage scenario

Data and AI MVP in a few weeks

Validating a data or AI product idea in a few weeks with a light MVP, before committing to full development.

Describe your project See the solution

Project overview

This scenario illustrates what we build; it is not a client reference.

This scenario shows how a startup can validate a data or AI product idea in a few weeks. The MVP sticks to the essentials, on a light and inexpensive architecture. It is used to convince first customers and investors before committing to full development.

Client challenges & requirements

A startup has an idea for a B2B service based on data analysis, but little funding.

It needs to show a working prototype to investors and first customers to check market interest, without starting a long and costly development.

Main objectives

Translate a business idea into technical specifications for an MVP

Deliver a first working prototype in a few weeks

Set up a lightweight and inexpensive architecture capable of demonstrating concept value

Shorten time-to-market and prepare a first funding round

Project solution

The approach is agile and lean

A short scoping workshop sets the essential scope. A first version of the machine learning model is built quickly.
Streamlit provides an interactive web interface. The MVP is deployed on inexpensive cloud infrastructure (Heroku, Firebase).

Indicators to track

Time from scoping to first demo
Total cost of the MVP
Customer meetings and letters of intent obtained
Hypotheses validated or invalidated

Tech stack

Web prototyping: Streamlit
Data Science: Python Pandas Scikit-learn
Backend & DB: Firebase (Functions, Firestore)
Deployment: Heroku Docker

Frequently asked questions

An MVP (Minimum viable product) focuses on essential features that validate key product hypotheses. It is developed quickly with prototyping-appropriate technologies, aiming to test the market with minimal investment before committing to full development.

Cost depends on the complexity of the product and the data available. The MVP approach deliberately limits scope to keep the budget small. Prisma Soft provides a quote after a scoping workshop.

After MVP delivery, you collect user feedback and validate your hypotheses. We can then support you in the next phase: iterating on the MVP, building a production-ready version, or scaling the infrastructure based on identified needs.