AI Systems

AI systems built around real workflows

A capable model is only one component. Loom Labs designs the surrounding workflow: grounded inputs, bounded actions, evidence, safe failure, and human review where judgment or risk remains.

Where AI usually breaks

The model is rarely the problem. Today's models are capable and commoditized. The hard part is everything around them: the workflow the model lives inside, the data it has to reach, the memory it loses between sessions, and the quiet failures nobody catches until a customer does. A prototype that wins the meeting still falls apart in production, because nothing was built to keep it reliable once real inputs and real edge cases show up. The intelligence was never the bottleneck. Making it work is.

How Loom Labs builds them

We start from how you actually work, then design the smallest bounded system around it. In practice that means grounded retrieval over approved sources, structured tools instead of hopeful one-shots, explicit human review, and a visible record of what happened. We build only what the work needs and document the operating boundaries before anything is activated.

What you get

A scoped system design around one process: clear inputs and outputs, evidence, review steps, and fallbacks for uncertainty. One example is document intake that extracts approved facts, drafts a first response, and routes anything uncertain to a person. Ownership, licensing, hosting, support, and handoff are stated in the signed order rather than assumed. Loom, our open-source coordination layer, is a public example of how we structure multi-agent software work.