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‣ VLM Run: The Unified Gateway for Visual AI

VLM Run is an end-to-end platform for developers to fine-tune, specialize, and operationalize Vision Language Models (VLMs). We aim to make VLM Run the go-to platform for running VLMs with a unified structured output API that’s versatile, powerful and developer-friendly.

VLM Run is a unified API to run all modern visual AI tasks.

• 📦 Unified Vision API: Harness the power of VLMs for tasks like OCR, tagging, image captioning, visual recommendations, and search—all under one roof.
• {…} ETL-Ready: Designed for visual ETL, our models extract JSON from diverse visual content—images, videos, presentations, and more—easily and accurately.
• 🎯 Hyper-Specialized: Fine-tune models for specific verticals or use cases within hours, ensuring enterprise-level outcomes with SLAs.
• 🛡️ Private: Deploy and operationalize visual AI securely in your private cloud, keeping sensitive data protected.

🧑‍💻 Why join us?

The founders, Scott and Sudeep are veteran AI experts with over 20+ years of cumulative experience in academia and industry. Scott worked on ML compilers at Meta, and model deployment at Tesla Autopilot, DeepScale and Cruise. Sudeep led ML infrastructure and engineering teams at Toyota Research, and has a CS PhD from MIT. We’re backed by leading venture-capital firms based in Silicon Valley and New York.

• Work with domain experts: From self-driving cars and AR/VR to robotics, we’ve tackled cutting-edge computer vision challenges in academia and industry.
• Build with AI-native tools: We’re constantly re-imagining our developer-stack with tools like Cognition Labs’ Devin, Cursor (with o1 + Sonnet) and Deep Research to 100x our developer leverage and ship faster.
• Work on the next frontier for visual AI: We believe that VLMs will make the last decade of computer-vision methods obsolete. In 5 years, using ConvNets or OCR will be like using a fax machine, and more than 90% of computer-vision workloads will be powered by VLMs.

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Send us your Github profile with links to popular repos/work to [email protected].

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