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Easify AI answers questions about a company's database in plain English. We took it from an idea to a working MVP people could use.
Source: https://easifytechnologies.com/case-study/easify-ai/
Work SaaS & AI
Easify AI
AI MVP Development for a Database Querying SaaS
What we did
MVP Development
Industry
SaaS & AI
Built with
- WordPress
- React
About Easify AI
Easify AI is an innovative tech startup focused on providing AI-driven solutions for businesses looking to enhance automation and efficiency. Their mission is to leverage artificial intelligence to simplify complex processes, helping companies streamline their operations and make data-driven decisions more effectively.
What we did
Our team partnered with Easify AI to bring their vision of an AI-powered platform to life through the development of a Minimum Viable Product (MVP). We focused on building a scalable, user-friendly solution that would enable them to showcase their innovative automation tools and AI capabilities.
MVP Development: Delivered a fully functional, scalable MVP highlighting Easify AI’s key features.
AI Automation: Integrated core AI functionalities for automating business processes and providing data-driven insights.
Agile Approach: Used iterative development cycles to incorporate continuous feedback and refine the product swiftly.
User Experience: Prioritized an intuitive interface to ensure ease of adoption and interaction for end-users.
Market Entry: The MVP enabled Easify AI to launch quickly, gather user feedback, and attract early investor interest.
Future-Proof Design: Built the MVP with scalability in mind, allowing for future feature expansion and updates as the platform grows.
The challenge
The platform existed as a plan rather than a product, and the questions that mattered next could only be answered by users: whether the AI querying was genuinely easier than writing the query yourself, and whether anyone would pay for it. Building the full platform first would have answered those questions late and expensively.
The approach
We scoped an MVP around the core querying flow and left everything that could wait out of it. Development ran in short cycles so feedback changed the next iteration rather than a later release, and the interface was kept plain enough that a new user could reach a result without onboarding. The foundations were built to carry more later, so the parts deliberately left out were postponed rather than designed out. How we scope and price this kind of product, including evaluation and model running costs, is set out in our guide to AI MVP development.
The work
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