News for Agent

Daily business news: funding, M&A, launches

Product Launches

Sapien Invites Five US Robotics Teams to Test Proof of Quality for a Tesla Model 3

By ·

Key takeaways

  • Sapien is selecting five US robotics, world model and physical AI teams to test Proof of Quality on their own datasets, with 72 hours per challenge.
  • The first team whose evaluation fails can choose a Tesla Model 3, $25,000 in cash or a $25,000 charity donation.
  • Applications close October 28, and a $1,000 referral award goes to the person named on the winning team's application.

Sapien is inviting five US robotics, world model and physical AI teams to test its Proof of Quality evaluation system on their own datasets, with 72 hours for each evaluation, according to EIN Presswire. The first team whose challenge Sapien fails can choose a Tesla Model 3, $25,000 in cash or a $25,000 donation to an eligible charity.

How the Proof of Quality Challenge Works

Before each challenge begins, the team and Sapien agree on a fixed dataset, secure access arrangements, the scores and explanations Sapien must return, and measurable targets for completeness and accuracy against the team's expert judgments. Where relevant, the targets also specify an acceptable limit on missed critical defects. Sapien's 72 hours begin at an agreed time, once the dataset, access, acceptance criteria and expert reference judgments are ready.

At the end of the evaluation, the team's experts compare Sapien's findings with their reference judgments. Missing the deadline or an agreed evaluation target constitutes a failure, and finding data of poor quality is a valid result when the evaluation accurately identifies it against the agreed criteria. The five challenges run consecutively in a fixed order, and the first verified failure wins the single prize. If Sapien meets every deadline and evaluation target, the prize remains unawarded.

"Got a dataset your team dreads reviewing? Tell us what makes it difficult and what good looks like. We'll agree on the dataset, evaluation output and acceptance criteria before the clock starts," said Rowan Stone, CEO of Sapien.

Applications are open to eligible US robotics, world model and physical AI teams, with one entry permitted per organization. Sapien selects five teams based on relevance, difficulty and whether the proposed evaluation falls within its supported scope. Applicants provide a work email, the size and format of their dataset, and a description of the quality issue they want to assess. Participation is free and requires neither a purchase nor payment, and a $1,000 referral award goes to the person named as the referrer on the winning team's application, paid within three days of verifying the winner and the referral.

Sapien's Funding and Product Record

Sapien's akta.pro company records list two funding rounds: a $5 million seed in April 2024 with 8 investors and a $10.5 million seed in October 2024 led by Variant. The same records list a 2025 product launch, the Proof of Quality Alpha testnet on Base Mainnet with smart contracts deployed, and a 2026 roadmap covering a Builder SDK, Protocol Explorer, Computer Vision Verification Toolset and Data Sovereignty Architecture in the first quarter, followed by Quality Signal REST APIs and a PoQ Studio Dashboard in the second and third quarters.

Sapien's Earlier Data Quality Push

In August 2026, Trevor Koverko, co-founder and chief strategy officer of Sapien, argued that as AI agents move from chatbots to workflow automation, verification of data and decisions will become a core part of the AI stack, International Business Times reported. Sapien, a bootstrapped data labeling software startup founded in 2023, reached an annual revenue of $8.6 million without relying on external funding or venture capital, according to B2B SaaS Blog.

Challenge Dates and Referral Terms

Applications close on October 28, selected teams will be announced afterwards, and the first challenge will begin October 14 at 5pm Pacific Time. Teams can apply and read the official rules on Sapien's challenge page.

Companies in this story

Sources

Machine-readable: Markdown · JSON