Finalists
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What Made the Top 12 Different
The AI Venture Velocity Challenge rewards progress over polish. During Stage 2, the 27 semifinalists were challenged to move beyond early signals and show whether their ventures were becoming more credible, useful, and ready for real-world adoption.
The 12 finalists stood out because they tested consequential assumptions, got closer to customers and operating environments, and allowed evidence to change their products, markets, and business models. They did not simply build more. They used AI to learn faster and build in response to what they learned.
AI Use for Accelerated Learning
Teams continued using AI as a learning accelerator, not just as a product feature. They used AI to analyze customer evidence, benchmark technical performance, accelerate prototypes, test workflows, explore markets, and shorten the time between a question and a decision.
The strongest teams paired AI’s speed with human judgment, domain expertise, and direct market interaction. They demonstrated that AI can accelerate venture development but only when founders test the assumptions that matter.
What Students Did in Stage 2
During Stage 2, semifinalists submitted additional experiment logs documenting what they tested, what evidence they collected, what they learned, and how their ventures changed. Teams also submitted reflection videos describing their progress and how AI helped them move faster or make better decisions.
Judges reviewed each team’s original venture snapshot, Stage 1 evidence, Stage 2 experiment logs, reflection video, and supporting materials. The finalists distinguished themselves by connecting experimentation to meaningful venture progress, including sharper market focus, stronger technical validation, customer commitments, field deployments, and evidence of willingness to adopt or pay.
Selection Criteria
The Top 12 were selected through a judging process grounded in the published challenge rubric:
- Problem significance and opportunity quality
- Learning velocity and experimentation rigor
- AI integration and leverage
- Evidence of venture progress
- Responsible impact and ethical design
- Adaptive execution and coachability
These criteria helped judges identify teams that were not merely producing activity, but learning what must be true for their ventures to succeed and building credible evidence around those assumptions.
Overarching Themes
Stage 2 reinforced an important idea: AI makes building faster, but it does not eliminate the need to determine what is worth building.
The finalists used AI to shorten learning cycles while staying close to customers, experts, partners, and real operating environments. Their ventures are not finished, and each still has important assumptions to test. But they have demonstrated the judgment, adaptability, and learning velocity needed to make meaningful progress.
AI Venture Velocity Challenge Finalists
Do Si
University | College: The University of Tulsa | College of Engineering and Computer Science; Tulane University | School of Science and Engineering
Team: Vusal Karimov; Toghrul Azizli
Academic Profile: Ph.D. students in petroleum engineering and chemical and biomolecular engineering
Industry: Biotechnology and drug discovery
Venture Synopsis: Do Si is developing failure-aware AI to identify potentially toxic molecules earlier in drug development. The team advanced by challenging its own technical assumptions, improving explainability and privacy, and grounding the product more directly in pharmaceutical workflows and buyer needs.
EntroPINN
University | College: University of California, Berkeley | College of Chemistry; College of Computing, Data Science, and Society
Team: Nathan Liu; Dongha (David) Kim; Christopher Donahue
Academic Profile: Undergraduate students in chemical engineering and computer science
Industry: Industrial engineering software and energy efficiency
Venture Synopsis: EntroPINN is building a physics-informed AI copilot that helps engineers optimize industrial processes with fewer costly simulations. The team advanced through rigorous technical benchmarking, direct industry discovery, and a disciplined decision to narrow its scope when broader generalization did not hold.
Lobe AI
University | College: Texas A&M University | College of Engineering; Mays Business School
Team: Tiernan Lindauer; Shubh Bhakta
Academic profile: Undergraduate students in computer science, physics, and finance
Industry: Senior living sales technology
Venture Synopsis: Lobe AI provides an AI sales assistant that engages families visiting senior living websites, schedules tours, and prepares community sales teams for follow-up. The team advanced after translating customer discovery into a live paid contract, additional customer commitments, and a pathway to broader deployment.
MottoNote
University | College: University of Wisconsin – Madison | College of Computing and Artificial Intelligence
Team: Siddharth Singh; Viacheslav Ludenko
Academic Profile: Undergraduate students in computer science
Industry: Enterprise knowledge management
Venture Synopsis: MottoNote turns unstructured organizational information into a continuously updated knowledge graph that AI systems can use. The team advanced by measuring real customer behavior, increasing adoption, and allowing usage evidence to overturn a requested feature and reshape the product.
NEXUS
University: Baylor College of Medicine; Columbia University
Team: Shay Beheshti; Brooke Dirvin
Academic Profile: Graduate students in genetics and genomics and business development
Industry: Oncology and clinical trials
Venture Synopsis: NEXUS is developing an AI system that matches cancer patients with clinical trials based on their medical records. The team advanced after securing an institutional pilot commitment and adapting the product around clinical integration, governance, and the realities of healthcare adoption.
Oncera Health
University | College: University of Texas at Austin | College of Natural Sciences
Team: Rutu Ruparel
Academic Profile: Undergraduate student in molecular and cellular biology
Industry: Oncology survivorship and digital health
Venture Synopsis: Oncera Health is building a personalized digital platform to help cancer survivors manage recovery after treatment. The team advanced by connecting patient needs with reimbursement research, institutional pilot interest, and direct engagement with health system stakeholders.
OsageMD
University | College: Texas A&M University | Mays Business School
Team: Matthew Schaefer
Academic Profile: Graduate MBA student
Industry: Healthcare operations and legal technology
Venture Synopsis: OsageMD is building a HIPAA-compliant case-management platform for medical lien financing workflows. The team advanced by combining held-out technical testing with direct workflow research, economic-buyer interviews, and meaningful interest in paid pilots.
Parkevo
University | College: High Point University | Earl N. Phillips School of Business
Team: Evan Taylor; Brianna Stinespring
Academic Profile: Undergraduate students in finance and social media and digital communication
Industry: Smart mobility and parking technology
Venture Synopsis: Parkevo uses edge AI to provide real-time parking availability and utilization analytics. The team advanced through a signed university pilot, production-oriented technical deployment, and extensive discovery across several institutional parking markets.
Reliat
University | College: Texas A&M University | College of Engineering
Team: Alice Queiroz; Phuong (Alex) Dao
Academic Profile: Undergraduate students in electrical engineering and data engineering
Industry: Mining and industrial AI
Venture Synopsis: Reliat is developing AI-powered intelligence for critical mining and aggregate operations. The team advanced by narrowing its initial concept to a more valuable market entry point, securing an industry partnership and changing its architecture, pricing, and customer definition as evidence emerged.
Smart Swine
University | College: Texas A&M University | College of Agriculture and Life Sciences; College of Engineering
Team: Ziyuan Zhao; Yu Wang; Emmanuel Otchere; Rolando Alaniz
Academic Profile: Three Ph.D. students in poultry science and one graduate student in engineering technology
Industry: Agricultural technology and animal health
Venture Synopsis: Smart Swine uses precision livestock AI to reduce piglet mortality and improve outcomes for pork producers. The team advanced by moving years of research into a commercial-farm deployment, testing its pricing assumptions, and earning meaningful interest from operators.
SpeechRoot (formerly VoiceVault)
University | College: Texas A&M University | College of Engineering
Team: Zarik Khan
Academic Profile: Undergraduate computer science student
Industry: Assistive communication and digital health
Venture: SpeechRoot is building an AI voice-personalization layer for people who use augmentative and alternative communication devices or face permanent voice loss. The team advanced by responding to clinical feedback, earning a pilot commitment, establishing a reproducible voice benchmark, and exploring reimbursement and distribution pathways.
ZERO1 IO
University: University of Silicon Valley
Team: Aisha Medina; Suman Dangol
Academic Profile: Graduate students in the Master of Business Innovation program
Industry: Enterprise operations and manufacturing AI
Venture Synopsis: ZERO1 IO develops private AI agents for complex operational workflows. The team advanced after embedded customer observation led it away from a less-repeatable delivery model toward a focused manufacturing workflow and paid customer validation.
Event Details
Saturday, Sept. 26, 2026
Watch as 12 talented student teams from universities across the country present business plans that leverage AI to address real-world challenges in a competition for a $100,000 grand prize. Then, network with industry leaders and executives seeking to connect with tomorrow’s AI talent and breakthrough solutions.
RSVP NowItinerary
Breakfast: 8:30 a.m.
Finalist Presentations: 9:30 a.m.
Lunch: 11:30 a.m.
Finalist Presentations: 12:30 p.m.
Judge Deliberations: 2 p.m.
Award Presentations: 2:30 p.m.
Networking Reception: 3 p.m.
Location
Wayne Roberts ’85 Building, Texas A&M University
256 Olsen Blvd., College Station, TX 77843
Parking
Paid parking is available in Lot 72