Co-Founder
Narsent started from a simple question: could receivables data be turned into better day-to-day collection and cash decisions, rather than just another reporting dashboard?
The product focused on helping companies understand which receivables were becoming risky, what that meant for future cash flow, and where teams should act first.
Receivables teams often have plenty of transactional data but still rely on manual follow-up, static aging reports, and individual judgment to decide what deserves attention.
The problem was not only predicting whether a payment might be late. The harder part was turning that signal into something operational: which account should be prioritized, what risk was changing, how cash flow might be affected, and what action should follow.
We explored Narsent as a decision-intelligence layer for collection and cash operations.
The product combined:
As a co-founder, I worked across the parts of the project that sat between the business problem and the technical system.
My work included:
The core idea was to move beyond a single risk score.
Signals from customer behavior, receivables, payment history, and forecasting could be combined into a decision layer that answered three questions: 1. What is changing? 2. Why does it matter? 3. What should happen next?
The system was designed to connect analytical outputs with operational actions rather than leave predictions isolated in a model or dashboard.
Predictive risk models were one input into the broader decision system; the detailed modeling work will have its own separate Work entry.
A large part of Narsent happened outside the product itself. We spent time speaking with companies, testing use cases, understanding how finance and operations teams actually worked, and figuring out where the product could create enough value to justify adoption.
Customer discovery and process discussions included organizations such as İZSU, Enerjisa, TurkNet, Gdz Elektrik, Arz Portföy, and Eksim Ventures.
At the same time, Narsent entered several startup and accelerator programs. That brought the project into contact with mentors, founders, corporate stakeholders, ecosystem operators, and potential investors, and gave us a much more practical view of how an early-stage company develops beyond the product.
Narsent gave me a close look at the distance between an interesting technical idea and a product a company might actually adopt.
It forced me to think about modeling, product design, customer workflows, commercial value, and implementation as parts of the same problem.
The project eventually ended, but it became one of the experiences that shaped how I approach business and technical problems today.