Kobie LaunchPad 2026 is a university AI hackathon open to engineering, computer science, and data science students across India, running from May through July 2026. Teams of 2–3 students compete across Machine Learning, Generative AI, and Cloud Engineering tracks for a chance to get mentored by expert Kobians, win cash prizes, and a chance at a real internship offer at Kobie. The Grand Finale takes place in Kobie’s new Bengaluru office, where finalists present live to Kobie’s Executive Leadership Team.
What is Kobie LaunchPad
Kobie LaunchPad is a university AI hackathon hosted by Kobie — a global Forrester leader in loyalty technology and services — that challenges student teams from India’s top universities to build real-world AI solutions across Machine Learning, Generative AI, and Cloud Engineering, with internship offers awarded to standout performers.
LaunchPad was designed to go beyond traditional campus recruiting. Rather than reviewing resumes, Kobie watches students build. The best performers earn a real, direct path into working at Kobie alongside the best and brightest in the Loyalty world.
Why Did Kobie Launch This Program?
The loyalty industry is being reshaped by AI — from personalized rewards engines to predictive engagement and intelligent data platforms. Kobie needs engineers and data scientists who don’t just understand AI in theory but can apply it to real problems.
LaunchPad exists because passive recruiting isn’t enough. We wanted a program that lets talent prove itself, so we built an environment where students can do exactly that.
It’s also an investment in India’s technical community. Kobie’s Bengaluru office is a hub of engineering excellence, and LaunchPad is how we find the people who will help grow it.
Who Can Participate in Kobie LaunchPad?
LaunchPad is open to students enrolled at partner universities across India, studying engineering, computer science, or data science.
Students compete in teams of 2–3. Each team selects one of four challenge tracks across the three following categories:
- Machine Learning
- Generative AI
- Cloud Engineering
University faculty coordinate registration and evaluate Phase 1 proposals using a Kobie-developed rubric. If your university is a partner, speak to your faculty coordinator to register.
How Does the Hackathon Work?
LaunchPad runs in two phases:
Phase 1 — Propose & Qualify
Teams submit a solution proposal for their chosen challenge. Faculty evaluate all proposals. The top 2 teams per challenge track advance to Phase 2.
Phase 2 — Build & Present
Qualifying teams build and submit their full AI solution. Each team will be given a Kobie mentor to help them think through the build-out. The strongest submissions are selected for the Grand Finale.
Grand Finale — At Kobie Office in Bengaluru
Finalist teams travel to Kobie’s Bengaluru office and present their solutions live to Kobie’s Executive Leadership Team. Winners are announced and prizes awarded on the day.
What Do Winners Receive?
Prizes are designed to reflect how seriously Kobie takes this program. Top performers are eligible for:
- Internship at Kobie — awarded to standout performers as a genuine career pathway
- Mentorship from Kobie engineers, data scientists, and product leaders throughout the competition
- Cash prizes for top-finishing teams
- Kobie swag and public recognition
- The opportunity to present directly to Kobie’s Executive Leadership Team in Bengaluru
The internship offer is the centerpiece. It’s not a symbolic reward — it’s a real role at a global technology company, extended to the participants who demonstrate exceptional thinking and execution.
What Challenges Will Teams Be Solving?
LaunchPad challenges are drawn from the kinds of problems Kobie’s own engineering and data teams work on. This isn’t a classroom exercise.
Each track — Machine Learning, Generative AI, and Cloud Engineering — has its own specific challenge brief. Teams choose one track and build their solution around it. Full challenge details are available through each university’s faculty coordinator upon registration.
Why Does This Matter for Students?
Most hiring processes evaluate what you’ve done. LaunchPad evaluates what you can do.
Participants get three things that are hard to find elsewhere:
- Real-world AI problems — not toy datasets or hypothetical scenarios
- Direct mentorship from Kobie technology professionals during the competition
- A stage in front of executive leadership — finalists present at Kobie’s office in Bengaluru
For students who are serious about a career in AI, machine learning, or cloud engineering, LaunchPad is a competitive edge.
Frequently Asked Questions
Can I participate if my university is not yet a partner?
LaunchPad currently runs through partnering universities. If your institution isn’t listed, speak to your department head about expressing interest and follow Kobie on LinkedIn for announcements about future cohorts and new university partnerships.
Do I need prior professional experience to enter?
No. LaunchPad is open to current university students, regardless of internship or work history. You need a team of 2–3, a registered university, and a strong idea. The rubric rewards problem-solving, technical execution, and clarity of thinking.
What happens after the Grand Finale?
Top teams are invited to Kobie’s new Bengaluru office, live to the Executive Leadership Team. Winning teams will be awarded cash prizes and Kobie swag. Internship offers are extended to standout performers following the Grand Finale. Kobie’s team will be in touch with qualifying participants directly. All finalists receive feedback from the judging panel, and top teams are recognized publicly across Kobie’s channels.
Get Involved
Phase 1 submissions close mid-May, exact dates depending on university. If you’re a student at a partner university, register now through your faculty coordinator.
If you represent a university and want to explore a future LaunchPad partnership with Kobie, we’d love to hear from you.
Follow Kobie on LinkedIn for finalist announcements, winner reveals, and future LaunchPad cohort openings.









