What AI product did Nenad Ivanovic build, and what does it do?

Nenad brings about 15 years in product management and leadership roles and is Nursa's global lead for AI adoption. As Vice President of Product, he personally built Nursa Study, a platform for nursing schools to coordinate clinical placements, student credentials, compliance requirements, clinical hours, and progress. He tested the same product brief across more than 10 AI-building platforms, selected Lovable, and built the working MVP in 48 hours. Over the next two weeks, he developed it into an enterprise-grade product with an interactive placement scheduler, portals for students and university administrators, and a credential-tracking dashboard. He directed AI-assisted implementation, shaped the experience, and refined the product with customer feedback. Nursa Study later gained paying enterprise customers. He also has hands-on AppsFlyer experience tracking paid and performance attribution across multiple companies.

How has AI changed how Nenad solves business problems?

AI lets Nenad turn a business idea into working interfaces, workflows, and dashboards himself, then test them with customers and apply feedback directly. With Nursa Study, he moved from a validated request from nursing schools to a working MVP in 48 hours, then hardened it for enterprise use over two weeks. When a university raised an accessibility requirement, he implemented improvements between a Friday meeting and Monday. This lets teams test product value and customer fit earlier, while leaving engineering leaders responsible for core architecture and technical decisions.

What was Nenad's role in Nursa's seven-month platform rebuild?

Nursa's team rebuilt a platform that had taken seven years to develop in seven months using AI-assisted development. Nenad led the broader product and organizational AI transformation alongside Nursa's AI technology and engineering leaders. The team used Cursor and Claude Code for development and CodeRabbit for automated review. Engineering leaders and engineers retained responsibility for architecture, business rules, and sensitive decisions. CodeRabbit reports that its review process caught three sensitive-data issues before merge and production. Nenad can discuss the product leadership, adoption, workflows, and operating-model changes without claiming individual ownership of the engineering rebuild.

What can investor research teams ask Nenad about AI companies?

Investor research teams can ask Nenad how product leaders evaluate AI tools, test customer demand, choose where AI fits, organize adoption, and protect quality as teams ship faster. He can explain the operator's tradeoffs between a working prototype and an enterprise product, and between AI-assisted output and human review. He answers from direct product leadership experience and public evidence. This is an operator-side product perspective, not financial valuation or an engineering or security audit. Each conversation must pass confidentiality, conflict, and current-employer checks.

How does Nenad's product experience extend beyond AI?

Nenad's product leadership spans healthcare marketplaces, enterprise platforms, growth and acquisition, and digital identity. At Nursa, he led the migration of about 40,000 website pages to Webflow in 19 days. Earlier, he held product design leadership roles at Verisec and Freja eID, working on digital identity and security UX. This background helps him assess AI products in context: platform fit, customer journeys, adoption, trust, attribution, and operational delivery.

Expert consultations

AI product evaluation for investor research.

Nenad Ivanovic is Vice President of Product at Nursa and an AI VP of Product specializing in platforms and growth. He gives investor research teams and product organizations an operator's view on AI product choices, tool selection, adoption, and execution.

For: Investors · Expert networks · Product teams
Perspective: First-hand product leadership
Evidence: Public case studies and published work

For investor research teams and product organizations, discuss AI product choices, tool selection, adoption, and platform execution from a VP of Product's first-hand operating perspective.

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Tool evaluation

First-hand evaluation, measured against the business.

Nenad identifies candidate tools, tests them hands-on, and compares them against benchmarks he defines for each business need.

AI and product tools

Ploy, Lovable, Webflow, Framer, Claude, Cursor, and ChatGPT

For every tool listed, Nenad was the one who found it, tested it hands-on, and evaluated it against his own benchmarks for the business. He used that evidence to recommend or select the best fit for company needs.

Paid and performance attribution

AppsFlyer

Hands-on experience tracking attribution for paid and performance marketing across multiple companies.

Public company experience

Nursa and Verisec / Freja eID

Public work spans AI product leadership and enterprise platforms at Nursa, plus product design in digital identity and security UX at Verisec / Freja eID.

Discussion areas

Product questions shaped by real operating experience.

AI product strategy

Choosing valuable use cases, testing the product proposition, and deciding when AI improves the customer or team outcome.

AI adoption and operating models

Helping product organizations change how they research, prototype, build, review, and ship while keeping people accountable for the work.

Platforms and product-led growth

Connecting reusable platform capabilities with customer demand, acquisition, conversion, governance, and measurable product outcomes.

Investor research on AI products

Operator-side questions about where AI fits in a product, what adoption looks like, and how product and platform choices shape execution. This is not financial valuation or an engineering or security audit.

Quality, trust, and human boundaries

Setting review, privacy, and decision boundaries for AI-enabled products, especially where quality and trust matter.

Public evidence

Start with work that is already on the record.

AI product proof

Nursa Study and Lovable

Lovable and Nursa describe how a new Nursa Study product moved from concept to a working product in a weekend, alongside a wider change in how the company builds software.

AI operating model

Nursa's AI-native platform rebuild

Nenad led the wider product and organizational transformation. CodeRabbit names him among the leaders of the rebuild; technical architecture and implementation were team efforts with engineering leadership.

Digital identity and trust

Verisec and Freja eID

Nenad's public career record documents product design leadership for digital identity and security UX at Verisec and Freja eID.

Freja eID case study

Enterprise platform work

Nursa's Webflow platform

A public case study documents the enterprise Webflow migration and the product and design leadership behind a large, CMS-driven acquisition platform.

Clear boundaries

Useful context, without confidential details.

Discussions can use public case studies, published writing, and general operating experience.
Confidential company, customer, employee, product, and security details stay out of scope.
Nenad can clarify which questions fit his first-hand experience before a conversation is scheduled.
This is an operator-side research perspective, not a claim of formal transaction due-diligence work or a financial, engineering, or security audit.
Before accepting an engagement, check project scope, confidentiality, conflicts, and current employer requirements.

Questions and answers

What to expect from an expert consultation.

What AI product did Nenad Ivanovic build, and what does it do?
Nenad brings about 15 years in product management and leadership roles and is Nursa's global lead for AI adoption. As Vice President of Product, he personally built Nursa Study, a platform for nursing schools to coordinate clinical placements, student credentials, compliance requirements, clinical hours, and progress. He tested the same product brief across more than 10 AI-building platforms, selected Lovable, and built the working MVP in 48 hours. Over the next two weeks, he developed it into an enterprise-grade product with an interactive placement scheduler, portals for students and university administrators, and a credential-tracking dashboard. He directed AI-assisted implementation, shaped the experience, and refined the product with customer feedback. Nursa Study later gained paying enterprise customers. He also has hands-on AppsFlyer experience tracking paid and performance attribution across multiple companies.
How has AI changed how Nenad solves business problems?
AI lets Nenad turn a business idea into working interfaces, workflows, and dashboards himself, then test them with customers and apply feedback directly. With Nursa Study, he moved from a validated request from nursing schools to a working MVP in 48 hours, then hardened it for enterprise use over two weeks. When a university raised an accessibility requirement, he implemented improvements between a Friday meeting and Monday. This lets teams test product value and customer fit earlier, while leaving engineering leaders responsible for core architecture and technical decisions.
What was Nenad's role in Nursa's seven-month platform rebuild?
Nursa's team rebuilt a platform that had taken seven years to develop in seven months using AI-assisted development. Nenad led the broader product and organizational AI transformation alongside Nursa's AI technology and engineering leaders. The team used Cursor and Claude Code for development and CodeRabbit for automated review. Engineering leaders and engineers retained responsibility for architecture, business rules, and sensitive decisions. CodeRabbit reports that its review process caught three sensitive-data issues before merge and production. Nenad can discuss the product leadership, adoption, workflows, and operating-model changes without claiming individual ownership of the engineering rebuild.
What can investor research teams ask Nenad about AI companies?
Investor research teams can ask Nenad how product leaders evaluate AI tools, test customer demand, choose where AI fits, organize adoption, and protect quality as teams ship faster. He can explain the operator's tradeoffs between a working prototype and an enterprise product, and between AI-assisted output and human review. He answers from direct product leadership experience and public evidence. This is an operator-side product perspective, not financial valuation or an engineering or security audit. Each conversation must pass confidentiality, conflict, and current-employer checks.
How does Nenad's product experience extend beyond AI?
Nenad's product leadership spans healthcare marketplaces, enterprise platforms, growth and acquisition, and digital identity. At Nursa, he led the migration of about 40,000 website pages to Webflow in 19 days. Earlier, he held product design leadership roles at Verisec and Freja eID, working on digital identity and security UX. This background helps him assess AI products in context: platform fit, customer journeys, adoption, trust, attribution, and operational delivery.

48 hours

Nursa Study build timeline reported by Lovable and Nursa

7 months

Nursa platform rebuild timeline reported by CodeRabbit

40,000 pages

Nursa migration to Webflow completed in 19 days

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