Excited to share that micro1 is now live on Microsoft Marketplace and officially Azure IP Co-Sell eligible. This means enterprises can now transact with micro1's Cortex solution through their existing @Microsoft relationship, with Microsoft handling the transaction and billing through Azure Marketplace. This helps streamline the procurement process for large organizations. Additionally, eligible purchases can decrement a customer's Microsoft Azure Consumption Commitment (MACC), enabling enterprises to use committed Azure spend to transact with micro1. At micro1, we believe enterprises must own their intelligence. As companies deploy more AI agents into real workflows, they need visibility into how those agents actually perform, where they fail, and how to improve them over time. micro1 Cortex provides enterprises with an evaluation stack that leverages expert human judgment to assess AI agents in real business workflows, helping teams identify why agents fail and monitor reliability as their models, prompts, and environments evolve. Thank you to the @msft4startups and @M12vc teams for the continued partnership.

We hosted our first virtual session with former Spirit Airlines employees to discuss our bid for the airline’s operational data. Privacy and security are core to this process. We’re only pursuing non-sensitive, non-consumer data, and are working to thoroughly de-identify it, with an independent third party involvement and very transparent development of our PII transformation model (more on this soon). This session was also a chance to hear directly from former Spirit employees, answer their questions, and address any concerns about how this data will be used. If there’s any questions, feel free to reach out to spirit@micro1.ai

Today we’re introducing flow, micro1’s next-generation data platform for turning human expertise into measurable capability gains. At the core of flow are Realms, micro1’s real-world RL environments where experts establish what strong performance looks like. flow-gen models expand human judgment into new environments, rubrics, variations, and edge cases, while flow-qc models evaluate performance, identify the highest-value failures, and route them back to experts for review. Each cycle produces a stronger training signal and a measurable gain in capability. Those gains compound across frontier models, enterprise agents through Cortex, and robotics, while improving the suite of data generation models recursively. We’re moving beyond producing data to delivering abundant & predictable units of intelligence improvement.

in the past 24 hours alone, micro1 has paid out $5.8M to 9 businesses for their enterprise data. that’s an average of over $600k per business. frontier AI needs training data that captures the complexity of real-world work. real businesses hold decades of decisions, exceptions and learnings that make the next breakthroughs possible. realism is a new dimension of scale & arguably the most important ingredient for model training data. if your company is interested in a data partnership, reach out to us.

Excited to share that micro1 has been selected to support the U.S. Department of Energy’s Genesis Mission. Genesis is a Manhattan Project-scale national effort to accelerate scientific discovery with AI and build the energy foundation needed to power America’s AI leadership. micro1 will support the mission by developing the data and evaluations that enable frontier AI to tackle some of the country’s most difficult scientific and engineering challenges. We’re proud to play a role in an effort this important to the future of American science, energy, and AI. More on our work with the DOE linked in the comments. 🇺🇸

What if AI research could create more culture, not less? Over two days, we sponsored more than 20 live performances across San Francisco, giving local musicians new stages and bringing more live music into public spaces around the city. Portions of those performances were also recorded to create real-world audio data for AI audio and music research. That data can help advance music education tools, improve how systems understand instruments and musical structure, and create new ways for emerging artists and music to be discovered. We believe nothing replaces human brilliance. The future of AI in music shouldn’t mean less human creativity. It should help people explore more, learn more, and bring more ideas to life.

Today we're publishing LongExtractBench, a benchmark commissioned by @reductoai and independently validated by micro1. We evaluated seven production document extraction systems across the same 225 complex enterprise documents. The benchmark was intentionally difficult: documents averaged 358 pages and contained roughly 88,700 ground-truth fields each. Every system was evaluated using the configuration documented in the benchmark methodology. Key findings: • Reducto Deep Extract was the only system to successfully complete all 225 documents. • Direct frontier LLM baselines achieved substantially lower completion rates on long, complex documents. • In this benchmark, dedicated extraction platforms achieved higher completion rates than the direct frontier LLM baselines. • Recall was the clearest differentiator. Precision remained high across systems, but recall ranged from 33.8% to 99.6%, highlighting which systems consistently captured the information contained in long, complex documents. The full report includes the benchmark methodology, limitations, and reproducibility resources. Check out the report and results in the comments below.

Experienced in financial planning, wealth management, or financial coaching? We're hiring professionals who can apply real-world judgment and client-first thinking to help train next-generation AI systems.

CFP or CFA with experience advising high-net-worth clients? 💰 Help train next-generation AI systems using your expertise in financial planning, investment management, retirement strategies, and wealth advisory.