AI Venture Velocity Challenge 2026 Dashboard

Texas A&M University — Mays Business School

AI Venture Velocity Challenge 2026

This dashboard tracks how student venture teams across 160 organizations moved from application to evidence, from evidence to semifinalist selection, and eventually to the finals at Texas A&M.

From Applications to Evidence

Story thesis: A different kind of student venture competition

The AI Venture Velocity Challenge was designed to test a different model of student venture competition: not who could polish the best pitch, but who could learn fastest, test harder assumptions, and use AI to accelerate real venture progress.

531 Applications Original applications received from student teams.
248 Stage 1 Teams Teams submitted at least one formal experiment log.
985 Formal Logs Google Form experiment-log submissions in the judge package.
27 Semifinalists Teams advanced from Stage 1 to Stage 2.

The Story This Dashboard Tells

The Starting Hypothesis

Students using AI thoughtfully should be able to test ideas, understand customers, and make venture decisions faster than traditional student venture timelines allow.

The Behavior We Wanted

Teams were asked to move from claims to evidence: identify assumptions, run experiments, document learning, and show how the venture changed.

The Pattern We Are Watching

The strongest teams were not simply more active. They tended to leave clearer evidence trails around learning velocity, customer understanding, and disciplined execution.

National Reach The challenge opened with 531 applications from 160 institutions, creating a broad field of student venture teams.

Evidence at Scale Stage 1 produced 985 formal experiment logs from 248 student venture teams.

Selection Lens The rubric placed its largest weight on learning velocity and experimentation rigor, not pitch polish alone.

How the Challenge Works

May 1, 2026-September 26, 2026: Teams move from venture snapshots to Stage 1 experiment logs, semifinalist selection, finalist selection, and the finals at Texas A&M.

1Applications: Student teams submitted venture snapshots.
2Stage 1: Teams documented experiments, evidence, and learning.
3Semifinalists: Twenty-seven teams advanced to the next stage.
4Top 12 Finalists: Finalists will be selected in early September.
5Finals at Texas A&M: Finalist teams will compete in College Station in September.

Evaluation Rubric

Learning Velocity and Experimentation Rigor
30%
Problem Significance and Opportunity Quality
25%
AI Integration and Leverage
20%
Evidence of Venture Progress
10%
Responsible Impact and Ethical Design
10%
Adaptive Execution and Coachability
5%

Supported by

The largest weight was placed on learning velocity and experimentation rigor, reflecting the challenge’s core focus: helping student founders test, learn, and build faster with AI.

Applications

Chapter question: Who showed up?

The challenge began with 531 applications from student venture teams representing 160 institutions. The mapped U.S. records show broad national reach, a wide mix of industries, and many different approaches to building with AI.

What The Application Pool Showed

What We Expected

If the challenge spoke to a real shift in venture building, student teams from many schools and sectors would respond.

What The Data Showed

The first pool included 531 applications from 160 institutions, with mapped records spanning every U.S. census region.

What Stood Out

The field was not limited to one industry or one kind of AI use. Teams applied AI across health care, energy, education, workflows, public problems, and consumer needs.

531 Original Applications Student venture applications received at launch.
160 Institutions Schools represented in the full original application pool.
438 Structured Snapshots Venture snapshots with structured stage and customer fields.
248 Stage 1 Teams Teams that moved from application into documented experimentation.

Application Footprint

The mapped application source shows broad participation across every U.S. census region.

Reading the counts: The headline application count is 531 from 160 institutions. The mapped institution tables represent 529 application records across 152 U.S. institutions available in the map source.

National Reach

Mapped Applications by U.S. Census Region
Region	Mapped Applications	Share	Institutions	Largest Source
South	323	61.1%	76	Texas A&M University
Northeast	77	14.6%	24	University of Connecticut
West	74	14.0%	28	Arizona State University
Midwest	55	10.4%	24	Northwestern University

Largest Application Sources

Top Institutions in the Original Application Map Source
Institution	Location	Applications	Share
Texas A&M University	College Station, TX	148	28.0%
Arizona State University	Tempe, AZ	15	2.8%
University of Connecticut	Storrs, CT	12	2.3%
New Jersey Institute of Technology	Newark, NJ	12	2.3%
Rice University	Houston, TX	10	1.9%
Northwestern University	Evanston, IL	9	1.7%
Virginia Tech	Blacksburg, VA	9	1.7%
University of California, Berkeley	Berkeley, CA	8	1.5%
Middle Tennessee State University	Murfreesboro, TN	8	1.5%
University of Texas at Dallas	Richardson, TX	8	1.5%
The Pennsylvania State University	University Park, PA	7	1.3%
Southern Methodist University	Dallas, TX	7	1.3%

View additional application mix details

Industries

Classified Venture Snapshots by Industry
Industry	Ventures	Share
Healthcare	95	21.7%
Industrial/Energy	87	19.9%
Productivity/Workflow	60	13.7%
Education	55	12.6%
Personal/Lifestyle	49	11.2%
Media/Creative	27	6.2%
Public Sector/Civic	25	5.7%
Finance	22	5.0%
Frontier/Deep Tech	10	2.3%
Other	8	1.8%

What Powered the Ventures

Common AI Patterns in Classified Venture Snapshots
AI Pattern	Mentions	Share
LLM Applications	300	69.1%
Decision Support	191	44.0%
Data and Analytics	121	27.9%
Workflow Automation	94	21.7%
Computer Vision	92	21.2%
AI Agents	88	20.3%
Voice AI	48	11.1%
Marketplace	28	6.5%
Search and Retrieval	21	4.8%
Robotics	18	4.1%

AI-pattern categories were identified in 434 readable snapshots. Teams could reflect more than one AI pattern, so these shares are not intended to add to 100%.

Venture Stage

Application-Stage Venture Maturity
Stage	Ventures	Share
Brand-New Idea	144	32.9%
Prototype	143	32.6%
MVP	91	20.8%
Has Traction	60	13.7%

Target Customers

Primary Customer Type at Application
Customer Type	Ventures	Share
Enterprise	147	33.6%
Consumer	138	31.5%
Small and Midsize Business	97	22.1%
Mixed Customer Types	28	6.4%
Government	16	3.7%
Research	12	2.7%

Stage 1 Experiment Logs

Chapter question: Who kept testing and learning?

Stage 1 asked teams to test assumptions, gather evidence, use AI as a learning accelerator, and document meaningful progress.

From Participation To Evidence

What We Expected

Some teams would treat the challenge like a deadline. Others would treat it like a learning cycle and use the full window to test, decide, and iterate.

What The Data Showed

Two hundred forty-eight teams submitted formal experiment logs, creating 985 records of venture learning during Stage 1.

What Stood Out

Semifinalists were not selected by log count alone. But the teams that advanced often gave judges a clearer trail of evidence, decisions, and progress.

Stage 1 Footprint

Teams that submitted experiment logs continued to represent a broad national field.

View Stage 1 institutions represented on the map

Stage 1 Institutions by Formal Experiment Logs
Institution	Location	Teams	Formal Logs	Share of Formal Logs
Texas A&M University	College Station, TX	65	252	25.6%
Virginia Tech	Blacksburg, VA	7	49	5.0%
University of Connecticut	Storrs, CT	7	35	3.6%
New Jersey Institute of Technology	Newark, NJ	6	27	2.7%
Florida International University	Miami, FL	2	26	2.6%
George Mason University	Fairfax, VA	5	23	2.3%
University of Texas at Austin	Austin, TX	4	19	1.9%
University of California, Berkeley	Berkeley, CA	5	18	1.8%
Arizona State University	Tempe, AZ	5	16	1.6%
Vanderbilt University	Nashville, TN	3	16	1.6%
The Pennsylvania State University	University Park, PA	4	15	1.5%
Duke University	Durham, NC	3	15	1.5%
Northwestern University	Evanston, IL	4	14	1.4%
University of Colorado Boulder	Boulder, CO	3	14	1.4%
University of Chicago	Chicago, IL	2	14	1.4%
University of Texas at Dallas	Richardson, TX	4	13	1.3%
Southern Methodist University	Dallas, TX	4	13	1.3%
Grambling State University	Grambling, LA	3	12	1.2%
Cornell University	Ithaca, NY	3	12	1.2%
Baylor College of Medicine	Houston, TX	1	12	1.2%
Middle Tennessee State University	Murfreesboro, TN	4	11	1.1%
Harvard University	Cambridge, MA	2	11	1.1%
University of Silicon Valley	San Jose, CA	2	10	1.0%
University of Miami	Coral Gables, FL	2	10	1.0%
Collin College	McKinney, TX	2	10	1.0%
High Point University	High Point, NC	1	10	1.0%
University of Texas at Arlington	Arlington, TX	2	9	0.9%
Spelman College	Atlanta, GA	2	9	0.9%
University at Buffalo	Buffalo, NY	1	9	0.9%
Denison University	Granville, OH	1	9	0.9%
Purdue University	West Lafayette, IN	3	8	0.8%
University of Alabama	Tuscaloosa, AL	2	8	0.8%
Boston University	Boston, MA	2	8	0.8%
Tulane University	New Orleans, LA	1	8	0.8%
Florida State University	Tallahassee, FL	1	8	0.8%
University of Texas at Tyler	Tyler, TX	2	7	0.7%
University of New Hampshire	Durham, NH	2	7	0.7%
Western Washington University	Bellingham, WA	1	7	0.7%
The University of Tulsa	Tulsa, OK	1	7	0.7%
Iowa State University	Ames, IA	1	7	0.7%
Dakota State University	Madison, SD	1	7	0.7%
University of Maryland Global Campus	Adelphi, MD	2	6	0.6%
University of Hawaii at Manoa	Honolulu, HI	2	6	0.6%
Rice University	Houston, TX	2	6	0.6%
University of North Carolina Chapel Hill	Chapel Hill, NC	1	6	0.6%
Saint Joseph’s University	Philadelphia, PA	1	6	0.6%
Polk State College	Winter Haven, FL	1	6	0.6%
Midwestern State University	Wichita Falls, TX	3	5	0.5%
University of Iowa	Iowa City, IA	1	5	0.5%
University of Tennessee, Knoxville	Knoxville, TN	1	5	0.5%
Pittsburg State University	Pittsburg, KS	1	5	0.5%
Texas Christian University	Fort Worth, TX	2	4	0.4%
Keck Graduate Institute	Claremont, CA	2	4	0.4%
Hampton University	Hampton, VA	2	4	0.4%
Wayne State University	Detroit, MI	1	4	0.4%
University of Wisconsin – Madison	Madison, WI	1	4	0.4%
University of Washington	Seattle, WA	1	4	0.4%
University of Texas Rio Grande Valley	Edinburg, TX	1	4	0.4%
University of Houston-Downtown	Houston, TX	1	4	0.4%
University of Arkansas at Pine Bluff	Pine Bluff, AR	1	4	0.4%
Texas State University	San Marcos, TX	1	4	0.4%
Southern New Hampshire University	Manchester, NH	1	4	0.4%
Snow College	Ephraim, UT	1	4	0.4%
Johns Hopkins Bloomberg School of Public Health	Baltimore, MD	1	4	0.4%
University of California, Davis	Davis, CA	2	3	0.3%
University of Southern California	Los Angeles, CA	1	3	0.3%
University of Rhode Island	Kingston, RI	1	3	0.3%
University of Notre Dame	Notre Dame, IN	1	3	0.3%
Stevens Institute of Technology	Hoboken, NJ	1	3	0.3%
South Louisiana Community College	Lafayette, LA	1	3	0.3%
San Diego State University	San Diego, CA	1	3	0.3%
Princeton University	Princeton, NJ	1	3	0.3%
Prince George’s Community College	Largo, MD	1	3	0.3%
Menlo College	Atherton, CA	1	3	0.3%
Florida Atlantic University	Boca Raton, FL	1	3	0.3%
Emory University	Atlanta, GA	1	3	0.3%
Dallas Baptist University	Dallas, TX	1	3	0.3%
College of Marin	Kentfield, CA	1	3	0.3%
California State University, Sacramento	Sacramento, CA	1	3	0.3%
California State University, Northridge	Northridge, CA	1	3	0.3%
University of California, Los Angeles	Los Angeles, CA	2	2	0.2%
New York University	New York, NY	2	2	0.2%
Washington University in St. Louis	St. Louis, MO	1	2	0.2%
Stanford University	Stanford, CA	1	2	0.2%
Robert Morris University	Moon Township, PA	1	2	0.2%
Oral Roberts University	Tulsa, OK	1	2	0.2%
Georgetown University	Washington, DC	1	2	0.2%
Drexel University	Philadelphia, PA	1	2	0.2%
Clemson University	Clemson, SC	1	2	0.2%
Yale University	New Haven, CT	1	1	0.1%
Xavier University of Louisiana	New Orleans, LA	1	1	0.1%
University of North Carolina Wilmington	Wilmington, NC	1	1	0.1%
University of Houston	Houston, TX	1	1	0.1%
University of Cincinnati	Cincinnati, OH	1	1	0.1%
Southeastern Oklahoma State University	Durant, OK	1	1	0.1%
Morgan State University	Baltimore, MD	1	1	0.1%
Loyola University of Maryland	Baltimore, MD	1	1	0.1%
Carnegie Mellon University	Pittsburgh, PA	1	1	0.1%
California State University East Bay	Hayward, CA	1	1	0.1%
Brigham Young University	Provo, UT	1	1	0.1%
Boston College	Chestnut Hill, MA	1	1	0.1%

Semifinalists Built a Longer Evidence Trail

The teams that advanced were not simply more active. They were more likely to document learning across multiple weeks, giving judges a clearer view of how assumptions, tests, decisions, and ventures changed over time.

Average Logs per Team 7.0 Semifinalists 3.6 Other Stage 1 teams Semifinalists documented almost twice as much formal learning.
Active in 4+ Weeks 48.1% Semifinalists 8.1% Other Stage 1 teams Semifinalists were far more likely to keep documenting progress across multiple weeks.
Median Evidence Span in Days 27 Semifinalists 1 Other Stage 1 teams The typical semifinalist left a longer trail between first and last log.

Full Stage 1 Comparison

Evidence Trail Metrics Across All Stage 1 Teams
Metric	Semifinalists	Other Stage 1 Teams
Avg. logs/team	7.0	3.6
Median active weeks	3 weeks	1 week
Active in 4+ weeks	48.1%	8.1%
Median first log date	May 24	June 7
Median evidence span	27 days	1 day
Final-week log share	40.4%	60.5%

The median evidence span measures the time between a team’s first and last submitted experiment log. It does not mean other teams only worked for one day; it means their submitted evidence was more compressed near the end of Stage 1.

Consistency Check Among Active Teams

Same Metrics Among Teams With 4+ Formal Logs
Metric	Semifinalists	Other Teams
Avg. logs/team	7.2	5.8
Median active weeks	3.5 weeks	2.0 weeks
Active in 4+ weeks	50.0%	18.2%
Median first log date	May 25	June 5
Median evidence span	24.7 days	13.9 days
Final-week log share	40.3%	61.1%

This view checks whether the pattern remains after excluding teams with fewer than 4 formal logs. The pattern still holds: semifinalist evidence was spread across more weeks and was less concentrated in the final deadline rush.

Log Submission Pattern

Formal Experiment Logs Per Team
Group	Teams	Average	Median	Maximum
All Stage 1 teams	248	3.97	4	25
Semifinalist teams	27	6.96	6	25
Other Stage 1 teams	221	3.61	3	16

Log volume is not a score. Judges evaluated the quality of evidence, relevance of experiments, and decisions teams made from what they learned.

Final Submission Rush

Teams documented experiments from May 4 through the June 20, 2026, deadline, with many submissions arriving in the final days.

Final 24 Hours

232 logs, 23.6%

Final 48 Hours

390 logs, 39.6%

Final 72 Hours

449 logs, 45.6%

Final Week

569 logs, 57.8%

What Teams Submitted

Most Common Experiment-Log Types Across All Stage 1 Teams
Experiment-Log Type	Count	Share
Customer discovery/interview learning	175	17.8%
Prototype or MVP build	132	13.4%
AI-enabled product or workflow test	131	13.3%
Technical feasibility test	89	9.0%
Prototype or MVP test with users	65	6.6%
Strategic pivot or major decision	61	6.2%
Market or competitor research	55	5.6%
General progress update with evidence	47	4.8%
Problem validation	46	4.7%
Testing AI agents for key business functions (e.g., product recommendations, customer communication)	34	3.5%

What Semifinalists Submitted

Most Common Experiment-Log Types Among Semifinalists
Experiment-Log Type	Count	Share
Customer discovery/interview learning	39	20.7%
AI-enabled product or workflow test	31	16.5%
Technical feasibility test	18	9.6%
Strategic pivot or major decision	16	8.5%
Prototype or MVP build	15	8.0%
Responsible AI/ethics/privacy/risk test	14	7.4%
Sales or customer acquisition test	10	5.3%
Pricing or business model test	8	4.3%
Market or competitor research	7	3.7%
Problem validation	6	3.2%

Documented Evidence Trail

Log count was not a score by itself. The grouped view shows how much formal evidence judges had available when assessing learning velocity, experiment rigor, and progress.

1 Log

Semifinalists 0.0%

Other teams 23.1%
2-3 Logs

Semifinalists 3.7%

Other teams 32.1%
4-5 Logs

Semifinalists 40.7%

Other teams 26.7%
6-8 Logs

Semifinalists 29.6%

Other teams 13.6%
9+ Logs

Semifinalists 25.9%

Other teams 4.5%

Grouped Formal Logs Per Team
Logs Per Team	Semifinalists	Semifinalist Share	Other Teams	Other Share
1 Log	0	0.0%	51	23.1%
2-3 Logs	1	3.7%	71	32.1%
4-5 Logs	11	40.7%	59	26.7%
6-8 Logs	8	29.6%	30	13.6%
9+ Logs	7	25.9%	10	4.5%

Number of Formal Logs Submitted Per Team
Logs Per Team	All Teams	Semifinalists	Other Stage 1 Teams
1	51	0	51
2	41	1	40
3	31	0	31
4	45	7	38
5	25	4	21
6	17	6	11
7	13	2	11
8	8	0	8
9	6	2	4
10	2	1	1
11	2	1	1
12	5	2	3
16	1	0	1
25	1	1	0

Industries Across the Funnel

The same broad industry buckets are used across stages to show how the field changed from applications to Stage 1 experiment logs and semifinalist selection.

Healthcare

Applications 21.7%

Stage 1 25.0%

Semifinalists 29.6%
Industrial/Energy

Applications 19.9%

Stage 1 8.9%

Semifinalists 7.4%
Productivity/Workflow

Applications 13.7%

Stage 1 37.5%

Semifinalists 48.1%
Education

Applications 12.6%

Stage 1 8.5%

Semifinalists 0.0%
Personal/Lifestyle

Applications 11.2%

Stage 1 8.5%

Semifinalists 3.7%
Media/Creative

Applications 6.2%

Stage 1 4.0%

Semifinalists 0.0%
Public Sector/Civic

Applications 5.7%

Stage 1 1.6%

Semifinalists 3.7%
Finance

Applications 5.0%

Stage 1 2.0%

Semifinalists 0.0%
Frontier/Deep Tech

Applications 2.3%

Stage 1 2.8%

Semifinalists 7.4%
Other

Applications 1.8%

Stage 1 1.2%

Semifinalists 0.0%

Industry Representation by Challenge Stage
Industry	Applications	Application Share	Stage 1 Teams	Stage 1 Share	Semifinalists	Semifinalist Share
Healthcare	95	21.7%	62	25.0%	8	29.6%
Industrial/Energy	87	19.9%	22	8.9%	2	7.4%
Productivity/Workflow	60	13.7%	93	37.5%	13	48.1%
Education	55	12.6%	21	8.5%	0	0.0%
Personal/Lifestyle	49	11.2%	21	8.5%	1	3.7%
Media/Creative	27	6.2%	10	4.0%	0	0.0%
Public Sector/Civic	25	5.7%	4	1.6%	1	3.7%
Finance	22	5.0%	5	2.0%	0	0.0%
Frontier/Deep Tech	10	2.3%	7	2.8%	2	7.4%
Other	8	1.8%	3	1.2%	0	0.0%

Industry categories are grouped for public reporting and use the same taxonomy across stages.

Semifinalists

Chapter question: What changed as the field narrowed?

The semifinalist pool reflects the challenge rubric: problem significance, learning velocity, experimentation rigor, AI integration, evidence of venture progress, responsible impact, and adaptive execution.

What Advancement Signaled

What We Expected

The strongest teams would combine meaningful problems with better evidence, not just stronger storytelling or more mature starting points.

What The Data Showed

Twenty-seven teams advanced from Stage 1 after judges reviewed their original snapshots, experiment logs, supporting evidence, and documented progress.

What Stood Out

The semifinalist group remained geographically distributed, with Texas A&M representation slightly lower than its share of the original application pool.

Semifinalist Footprint

The semifinalist pool remained geographically distributed while narrowing to teams with stronger evidence of learning velocity and venture progress.

Institution Representation

Texas A&M represented 25.9% of semifinalist teams, compared with 26.2% of Stage 1 log teams and 27.9% of the full application pool.

Semifinalist Institutions Compared With Earlier Stages
Institution	Semifinalists	Semifinalist Share	Stage 1 Teams	Stage 1 Share	Applications	Application Share
Texas A&M University	7	25.9%	65	26.2%	148	27.9%
Virginia Tech	2	7.4%	7	2.8%	9	1.7%
University of California, Berkeley	2	7.4%	5	2.0%	8	1.5%
Purdue University	1	3.7%	3	1.2%	4	0.8%
The Pennsylvania State University	1	3.7%	4	1.6%	7	1.3%
Baylor College of Medicine	1	3.7%	1	0.4%	1	0.2%
University of Texas at Austin	1	3.7%	1	0.4%	6	1.1%
The University of Tulsa	1	3.7%	1	0.4%	2	0.4%
Rice University	1	3.7%	2	0.8%	10	1.9%
Cornell University	1	3.7%	3	1.2%	5	0.9%
University of North Carolina Chapel Hill	1	3.7%	2	0.8%	1	0.2%
Florida International University	1	3.7%	2	0.8%	3	0.6%
High Point University	1	3.7%	1	0.4%	1	0.2%
Pittsburg State University	1	3.7%	1	0.4%	1	0.2%
George Mason University	1	3.7%	5	2.0%	5	0.9%
University of Silicon Valley	1	3.7%	2	0.8%	2	0.4%
University of Wisconsin – Madison	1	3.7%	1	0.4%	1	0.2%
Southern Methodist University	1	3.7%	4	1.6%	7	1.3%
University of Connecticut	1	3.7%	7	2.8%	12	2.3%

Institution names are normalized for comparison. Multi-institution teams can create small differences between public roster counts and primary-institution counts.

Semifinalist Evidence Varied

Across the semifinalist group, teams tended to document more learning over more time. But that pattern was not a rule. Judges advanced teams based on the quality of evidence, learning, venture progress, and disciplined decisions.

Semifinalist Counterexamples

2 logs A team advanced with the fewest formal logs among semifinalists.
5 logs in one day A team advanced after submitting all formal logs on the June 20 deadline.

Non-Advancing Counterexamples

16 logs A non-advancing team submitted more formal logs than most semifinalists.
12 logs over 46 days A non-advancing team documented evidence across nearly the full Stage 1 window.

The takeaway: more frequent, more sustained documentation was common among semifinalists, but activity alone did not determine advancement.

Student Level

Student Level Among Stage 1 Teams
Level	Teams	Share
Undergraduate Student	127	51.2%
Graduate Student	117	47.2%
Community College/Technical College Student	2	0.8%
Not specified	2	0.8%

Semifinalist Student Level

Student Level Among Semifinalists
Level	Teams	Share
Graduate Student	14	51.9%
Undergraduate Student	13	48.1%

AI Experience

AI Experience Among Stage 1 Teams
Experience Level	Teams	Share
Building With AI	123	49.6%
Advanced Users	46	18.5%
Regular Users	40	16.1%
Mixed Across The Team	24	9.7%
Occasional Users	9	3.6%
Beginner	4	1.6%
Not specified	2	0.8%

Semifinalist AI Experience

AI Experience Among Semifinalists
Experience Level	Teams	Share
Building With AI	18	66.7%
Mixed Across The Team	4	14.8%
Regular Users	2	7.4%
Advanced Users	1	3.7%
Occasional Users	1	3.7%
Beginner	1	3.7%

Interpretation: Advancement did not simply reward polish, school affiliation, or venture maturity. The evidence suggests the semifinalist group more often combined substantive problem spaces with clearer learning trails, stronger experiment discipline, and better use of AI to accelerate testing and decision-making.

Meet the Semifinalist Teams

These 27 teams advanced from Stage 1 after judges reviewed venture snapshots, experiment logs, supporting evidence, and documented progress against the published rubric.

AeroGraphiX

Advanced materials/Filtration/Clean air/Infection control

University/College: University of Connecticut | College of Liberal Arts and Sciences
Team: Deep Shikha Srivastava; Douglas Adamson; Robert Williams; Brenden Ferland

AeroGraphiX is developing graphene-modified HEPA filters designed to improve filtration and antiviral performance. The team stood out by pairing advanced materials research with AI-enabled market learning, using customer discovery to redirect the venture toward more specific, higher-need environments.

Atra

Sports operations/Athletics technology

University/College: Pittsburg State University | Crossland College of Technology
Team: Leo Chauchard; Baptiste D’Hondt; Edouard Meurant

Atra is an operations platform for NCAA track and field programs, helping coaches manage rosters, training, equipment, travel, and meet logistics. The team stood out by learning from real athletic environments, accepting where user behavior challenged its assumptions, and refining the adoption path around coaches.

BusFactor

Enterprise knowledge/Workforce risk/M&A integration

University/College: Purdue University | College of Engineering
Team: Tayseer Abdeljaber

BusFactor helps organizations identify critical knowledge gaps before employee transitions put operations at risk. The team stood out in Stage 1 by moving beyond a clever analytics concept, pressure-testing the problem with business and technical stakeholders, and refining the venture around a sharper path to adoption.

ChelysSecurity

Cybersecurity/Autonomous penetration testing

University/College: Florida International University | College of Engineering & Computing
Team: Alejandro Almeida; Jibram Jimenez; Joel Dos Santos; Lucas Cox

ChelysSecurity is developing adaptive AI agents for autonomous penetration testing with safety controls and evidence-based guardrails. The team stood out by treating responsible autonomy as a design challenge, using practitioner feedback to refine how human oversight, scope, and trust should shape the product.

Concept Bytes

Operating-room operations/Medtech procurement/Healthcare workflow

University/College: University of North Carolina at Chapel Hill | School of Education
Team: Shelby McCormick; William Andrew (Drew) Wyatt

Concept Bytes turns paper-based surgical preference cards into digital intelligence for operating-room teams. The team advanced by listening deeply to frontline professionals, discovering a larger knowledge-transfer problem, and reshaping the venture around the workflows that matter inside the OR.

Dhi

Computer vision/Safety analytics/Infrastructure monitoring

University/College: Rice University | College of Engineering
Team: Dev Sanghvi; Ansh Dabral; Madhuvani Thatiparti; Venkata Sai Aneesh Thatiparti

Dhi turns existing cameras into edge-based safety agents for real-time detection, monitoring, and infrastructure intelligence. The team stood out by using AI to accelerate both product development and customer outreach, then converting that speed into real discovery, demos, and market feedback.

Do Si

Drug discovery/Biotech/Pharmaceutical R&D

University/College: The University of Tulsa | College of Engineering & Computer Science; Tulane University | School of Science & Engineering
Team: Vusal Karimov; Toghrul Azizli

Do Si is developing a failure-aware AI platform to help identify toxic molecules earlier in drug development. The team advanced by using expert feedback to challenge its own technical assumptions and reshape the product around the trust, explainability, and workflow needs of deep-tech users.

EntroPINN

Industrial process optimization/Energy efficiency/Engineering software

University/College: University of California, Berkeley | College of Chemistry; College of Computing, Data Science, and Society
Team: Nathan Liu; Dongha (David) Kim; Christopher Donahue

EntroPINN is building a physics-informed AI copilot to help process engineers simulate, debug, and optimize industrial systems. The team advanced by combining serious technical depth with extensive customer discovery, grounding its AI work in the daily friction and judgment of engineering users.

HumaniCore AI

Responsible AI/HR tech/Compliance

University/College: The Pennsylvania State University | Smeal College of Business
Team: Haley Friary-Schenk; Aly Orloff; Rich Avila; Craig Gutjahr; Anirudh Badia; Dennis Hollenbeck

HumaniCore AI is building a certification and governance platform for AI-driven employment decisions. The team advanced by treating trust, validation, and responsible AI as core product requirements, using Stage 1 to test both the technical system and the credibility needed for adoption.

InfoSavvy

Higher education/Academic advising/Student success

University/College: Southern Methodist University | Lyle School of Engineering
Team: Yaw Boateng; Ameen Zia

InfoSavvy is an AI-powered assistant that helps students access academic advising, campus resources, events, and opportunities. The team stood out by testing real student demand during moments of advising need, using usage behavior and university feedback to refine the product beyond a generic chatbot.

Interlinked

Climate resilience/Wildfire intelligence/Emergency response

University/College: University of California, Berkeley | College of Letters and Science
Team: Rehaan Bicha; Neel Shetty; Rushil Desai

Interlinked is building an AI wildfire intelligence platform for risk prediction, response planning, and emergency decision-making. The team advanced by moving beyond a simple dashboard idea toward the data infrastructure and decision workflows needed by utilities, emergency teams, and resilience partners.

IntuSense

Medtech/ICU infection detection/Hospital-acquired infections

University/College: Texas A&M University | College of Engineering
Team: Idris Hussain; Dunebari Mii; Caroline Behnke

IntuSense is developing a sensor-based approach to help detect early signs of ventilator-associated pneumonia. The team advanced by using expert input to question its technical path, compare alternatives, and pivot responsibly toward a more promising detection strategy.

Lobe AI

Senior living/AI sales operations/Real estate customer acquisition

University/College: Texas A&M University | College of Engineering
Team: Tiernan Lindauer; Shubh Bhakta

Lobe AI is an AI web setter for senior living communities that talks with families, books tours, and gives sales teams better context. The team stood out for fast AI-native execution, testing both the product experience and the sales workflow needed to make the tool useful for operators.

MottoNote

Enterprise knowledge/Manufacturing AI/Knowledge graphs

University/College: University of Wisconsin – Madison | College of Computing and Artificial Intelligence
Team: Siddharth Singh; Viacheslav Iudenko

MottoNote turns fragmented company knowledge into a structured knowledge graph that AI can reason over. The team stood out by narrowing a broad organizational-memory idea toward industrial settings, where fragmented knowledge creates immediate operational friction and clearer value.

NEXUS

Clinical trials/Oncology/Health AI

University/College: Baylor College of Medicine; Columbia University
Team: Shay Beheshti; Brooke Dirvin

NEXUS is developing an AI-powered system to help match cancer patients with relevant clinical trials more efficiently. The team stood out by combining technical prototyping with clinical validation, testing whether its system could handle the complexity and precision required in oncology workflows.

Oncera Health

Oncology survivorship/Digital health

University/College: University of Texas at Austin | College of Natural Sciences
Team: Rutu Ruparel

Oncera Health translates clinical guidance into personalized daily actions for cancer survivors after treatment ends. In Stage 1, the team used stakeholder discovery to refine how survivorship support could move from a meaningful patient need toward a more credible healthcare adoption path.

OsageMD

Legal-tech/Healthcare finance/Medical lien operations

University/College: Texas A&M University | Mays Business School
Team: Matthew Schaefer

OsageMD automates medical lien case management for personal-injury law firms and medical providers. The team advanced by bringing AI into a messy, document-heavy operational workflow, testing real records and process constraints rather than relying on a polished concept alone.

Parkevo

Smart mobility/Parking analytics/Computer vision

University/College: High Point University | Earl N. Phillips School of Business
Team: Evan Taylor; Brianna Stinespring

Parkevo uses edge AI vision to give drivers real-time parking availability and administrators better utilization insights. The team advanced by turning a familiar campus frustration into a broader mobility opportunity, testing stakeholder needs, technical constraints, and expansion potential.

RaizenAI

Hiring/Talent assessment/Developer platforms

University/College: George Mason University | College of Engineering and Computing
Team: Jagan Yetukuri; Abey Paul; Priya Mohan

RaizenAI replaces resume-first developer screening with proof-of-work hiring based on real projects and AI-assisted skill evaluation. The team advanced by testing the core belief behind the business, comparing traditional hiring signals with performance evidence and refining its approach based on what the data showed.

Rally Dynamics

Sports robotics/Training technology/Computer vision

University/College: Texas A&M University | College of Engineering
Team: Vishanth Ramamurthy Yoganand; Ryan Daly

Rally Dynamics is building an AI-powered badminton training robot for academies and competitive players. The team advanced by pairing robotics development with feedback from players and academy owners, using that learning to refine the business model, training use cases, and path to adoption.

RELAI

Hospital operations/Care coordination/Health systems

University/College: Cornell University | SC Johnson College of Business; Weill Cornell Medicine | Graduate School of Medical Sciences
Team: Vijay Raghunathan; Galina Borodulina; Alexandra (Aly) Abrams; Gustavo Avila Amat; Carly Skudin

RELAI is building a real-time hospital transfer coordination platform for critically ill patients who need specialized care. The team stood out by bringing business and medical perspectives together, using stakeholder discovery and workflow testing to refine a high-stakes healthcare operations problem.

Reliat

Industrial AI/Mining operations/Predictive maintenance

University/College: Texas A&M University | College of Engineering
Team: Alice Queiroz; Phuong (Alex) Dao

Reliat brings AI into the physical backbone of mining and aggregate operations, turning industrial monitoring data into faster, clearer maintenance decisions. The team stood out in Stage 1 by getting close to real operators, pressure-testing its assumptions in the field, and refining the venture around the problems that drive downtime, risk, and lost productivity.

SecRecon

Cybersecurity/Managed security services

University/College: Virginia Tech | Pamplin Business School
Team: Lokesh Lalwani

SecRecon uses agentic AI to verify vulnerabilities and turn noisy security scans into evidence teams can act on. The team stood out by focusing on trust and verification, not just automation, and by using security-stakeholder discovery to sharpen the product around false positives and triage burden.

SmartSwine

Agtech/Precision livestock/Animal health

University/College: Texas A&M University | College of Agriculture & Life Sciences
Team: Ziyuan Zhao; Yu Wang; Emmanuel Otchere; Rolando Alaniz

SmartSwine uses precision livestock AI to help reduce piglet preweaning mortality in farrowing environments. The team advanced by combining agricultural research, producer feedback, and field-oriented product development, showing how AI can support practical animal-health decisions on the farm.

Vitalia AI

Consumer health/Chronic illness/Quantified-self tools

University/College: Virginia Tech | Pamplin College of Business
Team: Will Boyer

Vitalia AI helps people track health patterns across symptoms, habits, and daily data. The team stood out by listening to chronic-illness and quantified-self communities, using user feedback to move away from weaker use cases and focus more tightly on urgent health frustrations.

VoiceVault

Digital health/Assistive communication/Voice AI

University/College: Texas A&M University | College of Engineering
Team: Zarik Khan

VoiceVault helps patients facing permanent voice loss preserve or recreate a personal voice for assistive communication. The team distinguished itself by pairing AI voice technology with clinician and user feedback, using real-world needs to guide which patients and workflows the product should serve first.

Zero1 IO

Enterprise AI agents / Operations automation

University/College: University of Silicon Valley
Team: Aisha Medina; Suman Dangol

Zero1 IO deploys private vertical AI agents that turn messy operational work into ready-to-review drafts for complex businesses. The team stood out by learning that enterprise AI adoption requires focus, narrowing from a broad platform idea toward specific workflows where customers feel the pain immediately.

What Comes Next

Chapter question: Can faster learning become stronger ventures?

The AI Venture Velocity Challenge will continue to track how student teams use AI to test assumptions, learn from evidence, and build stronger ventures as the field narrows.

The Next Test

Stage 2 Focus

Semifinalists will continue testing the assumptions most likely to determine whether their ventures can become real, durable businesses.

What To Watch

The next stage should reveal which teams can turn evidence into sharper positioning, stronger customer pull, better products, and more disciplined execution.

Final Report

After the finals, a deeper report can share patterns in what separated teams and examples of how AI changed the pace and quality of venture learning.

Upcoming Milestones

Stage 2: Semifinalist teams continue testing, learning, and documenting progress through August.
Top 12 Finalists: Finalists will be selected in early September.
Finals at Texas A&M: Finalist teams will come to College Station for the September finals.
Challenge Report: After the finals, a fuller report will share aggregate patterns, lessons from the challenge, and examples of how AI changed the pace and quality of venture learning.