Credasis, a North Dakota company building tools that help people trust their data, today announced that Dorman Bazzell has joined its Board of Directors. Bazzell served as North Dakota's first Chief Data Officer and brings more than three decades of data and technology leadership in public service and the private sector.

Bazzell led the State of North Dakota's enterprise information management and software development programs as its first Chief Data Officer from 2018 to 2021. He is now Chief AI Officer at BISBLOX, a venture studio in Mandan, North Dakota. Before public service, he spent more than 30 years in the private sector, including leading analytics practices at Capgemini and Hitachi Consulting and serving as CIO of Coin Acceptors.

Credasis builds VERITY, an instrument to enforce data credibility where stakes are high and lives are impacted, such as cybersecurity and medicine, so decision makers can trust their systems. Bazzell's experience building statewide data governance and trust in public data is a natural fit for that mission.

“Dorman has spent his career on a question we care about every day: can people rely on the data in front of them? He led that work for the State of North Dakota as its first Chief Data Officer. That is the same problem VERITY is built for, and we are grateful to have his experience and judgment on our board,” said Tony Mangnall, CEO of Credasis.

“I am thrilled to join the board. Credasis is filling a need that is long overdue in data science. When I saw the work they were doing, I knew I wanted to be a part of it, and I’m excited to help them build,” said Dorman Bazzell.

Credasis is US-owned and self-funded, and was co-founded by Tony Mangnall and Marty Oelrich. VERITY ranks #1 on the public FALCON H1 brain-computer interface leaderboard as of Oct. 29, 2026. Rankings change. The company is also developing VERITY NANO, on-device seizure detection for wearables, with independent sealed confirmation of its development results underway.

Credasis is pioneering new methods for decoding signals across cybersecurity, quantum computing, fusion equilibrium, and now brain-computer interfaces. Most systems have to be trained on how to behave in the presence of certain labeled data. Credasis systems need no training and no labeling. That approach just took #1 on FALCON, a public intracortical BCI benchmark hosted on EvalAI. The North Dakota company's VERITY BCI entry leads the board as of Sept. 21, 2026. Rankings change.

FALCON (Few-shot Algorithms for Consistent Neural decoding) tests how well neural decoders stay accurate across days, when brain signals shift and labeled recalibration data is scarce. That consistency gap is one of the blockers between lab demos and real-world iBCI use.

In practice, a brain decoder turns neural activity into action. For example, it can translate a brain signal into movement of the limbs on a mind-controlled cybernetic prosthetic. As of Sept. 21, 2026, Credasis AI (VERITY BCI) ranks #1 on the public FALCON leaderboard by Held Out R² Mean (0.677), ahead of the official FALCON baseline (0.629) and other public teams. The live board is at https://eval.ai/web/challenges/challenge-page/2319/leaderboard/5747.

Credasis says the result came from combining insights across psychology, motor neuroscience, and deep mathematics, then turning that mix into a practical decoder that holds up when conditions change.

“Getting to #1 on FALCON matters because the board measures something clinics and patients actually need: decoding that stays useful when the brain signal drifts,” said Tony Mangnall, Co-Founder and CEO of Credasis.

“We built a simplified model that demonstrates intelligence well beyond brute force methods,” said Marty Oelrich, Co-Founder and Chief Scientist of Credasis. “Our system works because it's closer to the reality of how the mind actually works.”

VERITY is Credasis's instrument for label-free measurement: it calibrates on a system's own normal behavior, then measures whether live data still matches that baseline. Credasis applies that idea across high-stakes domains, including industrial control systems, network security, brain-computer interfaces, quantum verification, and AI that stays grounded in real data and can show where its answers came from. The tools are designed to run small, on ordinary CPUs, and stay local.

Credasis also posts competitive results on other public technical boards, including the Fusion Equilibrium Challenge (NeurIPS 2026), and is seeking design partners and research collaborators who work with unlabeled, high-stakes data.