Shelled AI

AI models have outrun the tools around them: agents are expensive to run, hard to govern and locked to one vendor, so the enterprises that could use them most hold back. Shelled AI coordinates five specialist agents, architect, coder, tester, reviewer and deployer, in one workspace that turns a request into a reviewed pull request, on the cloud or local models the customer chooses.

Coding agents can now write most of a feature, and the hard part has moved to everything around them: what each agent is allowed to touch, how its work is checked, what it costs to run and whether a regulated company can let it near production at all. A single assistant in a chat window answers none of that, and a team that wires several together by hand ends up maintaining the plumbing instead of the product.

Shelled AI treats the agents as a team with a harness. A request such as a SaaS with email sign-in, a paid tier and a health endpoint is read by an Architect that plans against the repository, implemented by a Coder, exercised by a Tester, examined by a Reviewer against the goal and the test evidence, and handed on by a Deployer as a reviewed pull request, with the plan, the diff and the checks visible at every step. Models can run in the cloud or locally, which is where much of the cost saving comes from, and the whole stack from workspace to infrastructure is one connected system. The company is based in San Francisco, where it also sponsors and runs AI developer events.