Company thesis
Why We Started Locatail
The customer-service AI problem is no longer getting a model to answer. It is making the right workflow reliable, affordable, and deployable.
The gap between a demo and a system
Most agent demos are easy to love. A clean prompt produces a convincing answer, a tool call works, and the room can see the future. Production is less forgiving. The same workflow has to survive edge cases, shifting data, approvals, latency targets, and millions of repeated decisions.
Teams often pay cloud-model prices for every one of those decisions, even when the task is narrow and measurable. Cost grows with usage while confidence stays difficult to prove. That is the gap Locatail is built to close.
Compile the workflow, not the conversation
We treat a repetitive support workflow as a system that can be imported, simulated, evaluated, specialized, and deployed. The result is not a generally smarter model. It is a smaller agent trained against a bounded definition of success, with its tools, permissions, and confirmation points made explicit.
That distinction matters. It gives an operator something concrete to measure before rollout and something economical to run after rollout.
Start where the work is measurable
Our first proving ground is retail assistance: search a catalog, plan a basket, offer alternatives, and ask before changing anything. It is familiar enough to understand in seconds and constrained enough to evaluate rigorously.
Locatail is starting with retailers that have high-volume shopping conversations, clear outcomes, and the patience to measure what actually works. That is how Cartside earns its way from proof of concept to production.