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In healthcare, tedious, time-consuming tasks like medical coding, documentation and managing insurance forms cut into the time doctors can spend with patients. Sully.ai helps solve this problem by developing “AI employees” that can handle routine tasks like medical coding and note-taking. As the company’s platform scaled, its proprietary, closed source models created three bottlenecks: unpredictable latency in real-time clinical workflows, inference costs that scaled faster than revenue and insufficient control over model quality and updates. To overcome these bottlenecks, Sully.ai uses Basete

Latitude is building the future of AI-native gaming with its AI Dungeon adventure-story game and  upcoming AI-powered role-playing gaming platform, Voyage, where players can create or play worlds with the freedom to choose any action and make their own story. The company’s platform uses large language models to respond to players’ actions — but this comes with scaling challenges, as every player action triggers an inference request. Costs scale with engagement, and response times must stay fast enough to keep the experience seamless. Latitude runs large open source models on DeepInfra’s infere

Sentient Labs is focused on bringing AI developers together to build powerful reasoning AI systems that are all open source. The goal is to accelerate AI toward solving harder reasoning problems through research in secure autonomy, agentic architecture and continual learning. Its first app, Sentient Chat, orchestrates complex multi-agent workflows and integrates more than a dozen specialized AI agents from the community. Due to this, Sentient Chat has massive compute demands because a single user query could trigger a cascade of autonomous interactions that typically lead to costly infrastruct

Customer service calls with voice AI often end in frustration because even a slight delay can lead users to talk over the agent, hang up or lose trust. Decagon builds AI agents for enterprise customer support, with AI-powered voice being its most demanding channel. Decagon needed infrastructure that could deliver sub-second responses under unpredictable traffic loads with tokenomics that supported 24/7 voice deployments. Together AI runs production inference for Decagon’s multimodel voice stack on NVIDIA Blackwell GPUs. The companies collaborated on several key optimizations: speculative decod