TL;DR
- Robinhood CEO’s AI venture Harmonic launched a chatbot app powered by its model Aristotle.
- The company claims the model offers formally verified, hallucination-free answers in math-related domains.
- A recent $100M Series B round valued Harmonic at $875M.
- The startup aims to build Mathematical Superintelligence (MSI) for broader scientific use.
Aristotle App Now Available on Mobile
Harmonic, an AI startup co-founded by Robinhood CEO Vlad Tenev, has launched the beta version of its chatbot app for both iOS and Android. The app provides user access to its proprietary large language model, Aristotle, which the company touts as capable of hallucination-free mathematical reasoning.
CEO and co-founder Tudor Achim said in an interview with TechCrunch that Aristotle is the first AI system “available to people that does reasoning and formally verifies the output.”
“Within the domains Aristotle supports — which are quantitative reasoning — we actually guarantee there are no hallucinations,” Achim said.
Focus: Building a Mathematical Superintelligence
Harmonic positions Aristotle not as a general-purpose AI assistant, but as a step toward Mathematical Superintelligence (MSI) — a system that can deeply reason through complex domains that rely heavily on math, such as physics, computer science, and statistics.
Aristotle’s current capabilities are focused on math-intensive queries. Harmonic claims the system performs formal verification before generating answers, which helps avoid the hallucination problems found in most mainstream AI models.
For output verification, Aristotle uses the Lean programming language, and responses are double-checked via non-AI algorithms, similar to those used in medical devices and aviation systems.
Competitive Landscape and Olympiad Results
Harmonic reports that Aristotle achieved gold medal performance on the 2025 International Math Olympiad (IMO), using formally translated test problems. This sets it apart from other AI models — including those from OpenAI and Google DeepMind — which participated in informal language-based test formats.
Although no additional benchmarks were disclosed at this stage, Harmonic asserts that Aristotle is already demonstrating potential as a precision-based system in high-stakes environments.
Harmonic Key Data
| Metric | Detail |
| Product | Aristotle chatbot app |
| Platforms | iOS, Android |
| Funding | $100M Series B |
| Valuation | $875 million |
| Verification Method | Formal logic using Lean |
| Unique Claim | “Hallucination-free” mathematical outputs |
What’s Next for Harmonic?
In addition to the public app, Harmonic plans to launch a web-based interface and an API for enterprises, making Aristotle’s capabilities accessible beyond just mobile users. According to Achim, the company’s recent funding round — led by Kleiner Perkins — will help accelerate the rollout of its MSI vision.
The launch positions Harmonic in the growing race among AI firms to master verifiable reasoning, with math as a proving ground. Unlike other models that attempt to simulate reasoning through pattern-matching, Aristotle’s use of formal logic aims to offer guaranteed accuracy — a crucial edge in enterprise and academic applications.
Why Math-First AI Matters
Math, due to its binary nature and verifiability, is viewed as a gateway to mastering higher-level AI reasoning. Companies like OpenAI, Google, and now Harmonic are focusing on solving mathematical problems as a foundation for broader AI application across science, engineering, and finance.
Achim believes that if Aristotle can succeed at math, it will eventually be capable of navigating any structured reasoning task — potentially transforming not just education, but also enterprise analytics, scientific computing, and AI safety research.
Final Thoughts
While Harmonic’s “hallucination-free” claim remains bold and largely unproven outside internal testing, the launch of Aristotle marks a compelling step forward in the AI verification space. As the startup moves toward enterprise deployment and broader integrations, its success could shift expectations for how AI models are validated and applied in critical mathematical domains.
