Beam AI has launched BIM CoPilot, a fully managed building information modelling solution that provides human-vetted, multi-trade BIM management across architecture, structure, civil, HVAC, plumbing, electrical and fire protection. The launch expands the company's offering beyond preconstruction into the build phase. BIM CoPilot helps contractors convert design documents into coordinated 3D models, clash-free documentation and construction-ready drawing sets. The service is available as a standalone, project-based offering for all construction teams, with pricing based on project scope and complexity. Beam AI is already trusted by over 1,200 contractors and suppliers across the US and Canada for AI-based takeoff, estimating and bid management. The company is Series B backed by Insight Partners.

B.AI launches global platform for autonomous AI agents. B.AI launched a global infrastructure to run fleets of autonomous AI agents for research into artificial general intelligence. B.AI announced a global infrastructure designed to run autonomous AI agents across cloud regions and edge locations. The platform provides distributed compute, agent orchestration, data pipelines and evaluation tools to let many agents operate, learn and coordinate across regions. The platform enables researchers and engineers to deploy and manage fleets of agents that pursue long-running goals, interact with simulated and real environments, and share experiences for collective learning. It combines scheduling and autoscaling for agent workloads with monitoring, logging and experiment tracking to support large-scale research and production tests. Technical components include an orchestration layer that schedules agents and allocates compute, sandboxed runtime environments for safety and reproducibility, connectors to external APIs and hardware, and integrated simulation environments for training. The system supports containerized workloads and standardized APIs so teams can plug in different model backends, reward functions and evaluation suites without rewriting orchestration code. B.AI built the infrastructure to place workloads on public clouds, private data centers or edge nodes to reduce latency for interactive tasks and to meet data residency requirements. The architecture includes role-based access controls, encrypted data flows and auditing features to help organizations manage sensitive data and meet compliance needs. Developer tooling comprises an SDK for writing agent code, a web console for launching experiments and dashboards that display performance metrics and safety signals. The platform offers benchmark suites and replay facilities so researchers can reproduce past runs and compare results across model versions. Safety and governance features are integrated at the experiment level to limit agent permissions and stage rollouts. The system provides logging and red-teaming hooks for hazard analysis, options for isolating agents during early testing and mechanisms to expand environment access as confidence in behavior increases. B.AI said it will roll out access to partners, research groups and select customers while developing further controls and evaluation methods. The company cited ongoing work to standardize agent interfaces and expand simulation environments used to test complex tasks. Content on BlockPort is provided for informational purposes only and does not constitute financial guidance. Blockport STO strive to ensure the accuracy and relevance of the information Blockport STO share, but Blockport STO do not guarantee that all content is complete, error-free, or up to date. BlockPort disclaims any liability for losses, mistakes, or actions taken based on the material found on this site. Always conduct your own research before making financial decisions and consider consulting with a licensed advisor. For further details, please review its Terms of Use, Privacy Policy, and Disclaimer.