Nursa Study: 48-Hour AI Product Build

Nursa Study was initiated and built by Nenad Ivanovic, Nursa's VP of Product, using Lovable. He produced a working MVP in one weekend and spent the following two weeks developing it into an enterprise-grade platform for nursing schools.

The product became more than a fast launch. It gave Nursa concrete evidence that AI-assisted development could support serious enterprise software, helping trigger wider adoption across a company of more than 200 employees.

Nursing schools had been asking Nursa for a platform designed around clinical placements, student credentials, and the coordination required to move students from classrooms into care environments. The opportunity was validated, but a lean product, design, and engineering organization was already balancing the demands of a growing healthcare marketplace.

For Nenad, the problem was larger than one missing product. Even small ideas could require weeks of alignment across a team before customers could react to anything real. The organization needed a way to explore new business lines faster without treating architecture, accessibility, or engineering quality as optional.

Nenad tested the same product prompt across more than ten AI-building platforms. Lovable produced the strongest result, so he gave himself one weekend to answer a practical question: could an experienced product leader build the requested platform directly?

By Sunday night, he had a working Nursa Study MVP: an interactive clinical-placement scheduler, separate portals for nursing students and university administrators, and a credential-tracking dashboard. Nursa's own launch coverage explicitly identifies Nenad as the VP of Product who built Nursa Study with Lovable.

This was not a speculative design exercise. It was a functional product that could be placed in front of a university immediately, turning a persistent customer request into something concrete enough to test, critique, and improve.

After showing the MVP to Nursa CEO Curtis Anderson, Nenad spent two weeks developing it into an enterprise-grade product. Nursa Study became a live platform for coordinating clinical placements, compliance, credentials, hours, and student progress across schools, instructors, students, and placement sites.

The new workflow changed the customer-feedback cycle. Instead of documenting requests for a later release, Nenad could implement feedback between meetings. When a school raised the need for full WCAG accessibility standards on a Friday, the change was ready by Monday.

That speed did not remove product judgment. It made judgment more consequential: the quality of the problem framing, context, architecture, and review process determined what the technology produced.

The Nursa Study result gave the company evidence that AI-assisted development could support serious enterprise software. CEO Curtis Anderson subsequently encouraged all 200+ employees to build with Lovable, treating the capability as an opportunity for people across the company to redefine how they contributed.

Nenad and his team then used the approach to accelerate the rebuild of Nursa's core platform, helping deliver it in roughly seven months. Lovable's published case study describes that delivery as approximately twelve times faster than the prior operating model.

The change extended beyond product and engineering. Credentialing and finance specialists began building tools around their own domain knowledge, while Nursa identified roughly ten legacy SaaS systems for retirement. These are company-level outcomes, not Nenad-only achievements; his leadership significance is that the initial product proof helped make the wider shift credible and actionable.

The Nursa Study story demonstrates a practical model of AI-first product leadership: begin with validated customer demand, build a real proof quickly, put it in front of customers, preserve enterprise standards, and turn the learning into a repeatable system for the wider organization.

Software engineering remained essential. What changed was the interface between product intent and working software. Prototypes became executable, feedback cycles compressed, and product, design, and engineering could align around something they could inspect rather than a document describing what might eventually exist.

For Nenad, the result was larger than a 48-hour launch. A new Nursa business line became the catalyst for a company-wide change in how ideas moved from insight to production.