Automate the work your team shouldn't be doing by hand.

AI automation combines traditional workflow automation with language models that can read, understand and produce text. Where classic automation breaks on unstructured input like documents, emails and free-form data, AI automation handles it: extracting fields, making judgment calls with confidence scores, and routing exceptions to humans.

Those are excellent trigger-and-action pipes, and we sometimes build on them. The hard part is what happens inside the pipe: reliable extraction from messy documents, judgment steps that need evals, error handling, and human review flows. That engineering layer is what we build. You get the automation, not a subscription to configure yourself.

Most automations we build pay for themselves within months, because the baseline is salaried hours spent on repetitive work. A focused automation of one workflow is typically a few weeks of engineering at a fixed price. During discovery we calculate the ROI with you: hours saved, error rates reduced, and throughput gained, before you spend anything.

It depends on your documents, which is why we measure instead of promise. We label a sample of your real documents, build the pipeline, and report accuracy against that test set. Fields below your accuracy bar go to human review. Typical production systems run high-90s accuracy on structured fields with review queues catching the rest.

In practice it removes the worst hours of their week: the copying, the retyping, the triage. Teams redeploy that time to work that actually needs people. The systems we build keep humans in the loop by design, especially for approvals and edge cases.

Yes. Pipelines can run in your cloud accounts with your data never leaving your environment, and model choice respects your data-residency and privacy constraints.