Liquid AI, Inc.
Build state-of-the-art, general-purpose AI systems that are capable, efficient, highly aligned, and trustworthy, with a focus on edge and on-device deployment.
At a Glance
- Enterprise
- Biotechnology & Healthcare
- E-commerce & Retail
- Consumer Electronics
- +2 more
AI Tools by Liquid AI, Inc.
(1)
Liquid AI
Efficient On Device Foundation Models
Discussions
No discussions yet
Be the first to start a discussion about Liquid AI, Inc.
Latest News
No Cloud, No Waiting: Tool-Calling Agents on Consumer Hardware with LFM2-24B-A2B
Liquid AI and Insilico Medicine Partner to Accelerate Drug Discovery with LFM2-2.6B-MMAI
McKinsey & Company: The case for liquid foundation models
Liquid AI and Shopify announce multi-year partnership for sub-20ms foundation models
Products & Services
Initial generation of text and multimodal models (1.3B, 3B, 40B) optimized for efficiency.
High-performance small foundation models designed for speed and on-device deployment.
Developer platform for finding, customizing, and deploying AI models on any device (edge AI).
Extremely small models (350M-2.6B parameters) for smartphones, laptops, and embedded devices.
Market Position
Liquid AI positions itself as a more efficient, faster, and more private alternative to Transformer-based AI companies like OpenAI and Anthropic, particularly for edge computing and real-time enterprise workflows.
Leadership
Founders
Ramin Hasani
CEO of Liquid AI. Previously a postdoc at MIT CSAIL and PhD from TU Wien. Co-inventor of Liquid Neural Networks.
Mathias Lechner
CTO of Liquid AI. Previously a postdoc at MIT CSAIL and PhD from IST Austria. Expert in robust machine learning.
Alexander Amini
CSO of Liquid AI. PhD from MIT and lead researcher at MIT CSAIL. Specialist in end-to-end learning and autonomous systems.
Daniela Rus
Co-founder. Director of MIT CSAIL and Professor of Electrical Engineering and CS at MIT. World-renowned robotics expert.
Executive Team
Ramin Hasani
Chief Executive Officer
Co-inventor of Liquid Neural Networks; MIT CSAIL researcher.
Mathias Lechner
Chief Technology Officer
MIT CSAIL researcher; expert in robust AI and robotics.
Board of Directors
Founding Story
Spun out of MIT CSAIL by four researchers to commercialize 'Liquid Neural Networks' – a new class of biology-inspired, efficient AI models that outperform Transformers in speed and efficiency for sequential data.
Business Model
Revenue Model
Enterprise licensing, API usage fees, and strategic partnerships. Offers on-premise and edge deployment options.
Pricing Tiers
Full access to AI model search and deployment for developers.
Costs for input and output tokens via API.
Priority support, scaling solutions, and on-premise deployment.
Target Markets
- Enterprise
- Biotechnology & Healthcare
- E-commerce & Retail
- Consumer Electronics
- Automotive & Robotics
- Education
- Drug discovery and pharmaceutical research
- Real-time e-commerce experiences
- Edge robotics and autonomous agents
- Vision-language processing in AR/VR
- Enterprise-grade private AI solutions
- AI-enhanced education
- Shopify
- Insilico Medicine
- Capgemini
- G42