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Physical AI needs context about its surroundings to adapt beyond a fixed set of instructions. Munich-based Spranic is building a spatial memory layer that records physical spaces and how they change.
Enterprise AI scalability is running into a hard budget wall for some customers. As agentic workloads multiply, cloud token bills are consuming allocations faster than planned, prompting companies to rethink where inference should run.
Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it. BotMan AI uses an AI-powered migration pipeline that maps an application’s dependencies, business logic and data models in a knowledge graph.
A control plane for distributed AI could help applications decide which tasks to run on a device, at the edge or in the cloud. Seismora Inc. is developing networking technology to coordinate AI workloads across different providers and computing environments, according to Vito Palermo (pictured),…
Open-weight models keep multiplying, and the AI coding harness around them is becoming just as important to get right. Developers and enterprises are trading off cost against capability. The harness helps translate a model’s capabilities into useful work. Cline Bot Inc.
Qiagen N.V. is betting that drug discovery agents need a trustworthy knowledge foundation as much as capable models. For biopharma companies, that means giving agents information with clear provenance and enough context to support their answers.
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