Pranav Akshat

A Founder Learning Where Business, Data, and AI Meet.

A 21-year-old UT Dallas student in the 2026 graduating class, studying Business Analytics and Artificial Intelligence, building AI products for dealership problems that used to take too much labor to solve.

Read My Story See My Background

2026 UT Dallas Business Analytics and AI

4 Fields Automotive, Consulting, Sales, Marketing

Serfis AI Employees for Real Workflows.

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First chapter

The founder story starts with a practical question: What can a business actually do with the data it already has?

Pranav began college at Indiana University as an entrepreneurship major, then transferred to UT Dallas during sophomore year and chose business analytics and artificial intelligence. The shift was intentional. Companies store more data than they know how to use, and the opportunity is bigger than dashboards: it is turning information into better decisions, better timing, and better customer experiences.

Backstory

Built from the overlap of data, operations, and user experience.

Entrepreneurial roots

Pranav came into college drawn to entrepreneurship, but realized that being entrepreneurial did not have to mean studying only entrepreneurship. Business analytics offered a stronger base for understanding how companies collect, store, and act on information.

Data with a better experience

Growing up around his dad's user-experience work shaped an early point of view: data should help a business serve people better, not make customers feel like their information was simply taken.

Real-world pattern recognition

Through automotive work, supply-chain project management, consulting, sales, and marketing, Pranav saw the same problem repeat. Businesses had useful information, but the work required too many hours for busy teams to execute consistently.

The dealership insight

While consulting dealerships on business practices, he noticed common problems across large stores that were unsolved because the labor model did not scale. AI changed that equation.

From experiments to products

He started building projects, games, algorithmic trading models, and eventually real products. Serfis began as an outreach product, then sharpened toward dealership re-engagement so a store can earn the second, third, and fourth purchase.

Experience

Four operating lenses from college work that shaped the product.

Automotive

More than a year around dealership environments, seeing how sales, service, follow-up, and customer relationships actually move.

Consulting

A year of diagnosing business practices and translating messy operations into sharper recommendations.

Sales

A year learning the daily reality of outreach, trust-building, objection handling, and timing.

Marketing

A year connecting audience, message, and conversion, with a bias toward follow-up that feels relevant instead of random.

Founder thesis

AI Should Not Replace People. It Should Take On the Work They Do Not Have the Bandwidth to Do.

The goal is to give businesses AI employees that handle repetitive, time-consuming workflows people either do not have time for or should not have to do by hand. That lets teams focus on judgment, relationships, creativity, and the work that actually MATTERS!