Discovery beats documentation
The best AI opportunities are rarely visible from the outside. Sitting next to the work surfaces the friction, the exceptions and the handoffs that no process map records, and that is where the real wins hide.
The entry point
Two weeks, one workflow, a working AI employee.
The best automation is rarely visible from the outside. So we do not start with a proposal. We sit next to the work.
A Discovery Pod pairs one of our AI engineers with the person doing the job for two weeks. You leave with a running agent on your real systems and a number: its accuracy and cost per outcome. Fixed fee, and valuable even if we stop there.
The ten days
Shadow, prioritize, build, ship.
Shadow · Days 1 to 2
We sit with the person doing the job, watch every step, and document the real workflow, the one with the workarounds and the tribal knowledge, not the one in the process doc.
Prioritize · Day 3
We rank the opportunities by scale, repetition, business impact and data availability. The workflow is the unit of automation, not the task, so we look for the handoffs and approvals worth removing, not just steps to speed up.
Build with · Days 4 to 7
We build a working agent alongside the person whose job it is, on your real systems and your real cases, not a sandbox. They shape it as it takes form, so it fits the work instead of forcing the work to fit it.
Validate and ship · Days 8 to 10
We prove it generalizes across several others doing the same work, wire the evals and the human-review gate that keep it honest, and ship it. You get a number: its accuracy and cost per outcome on your data.
What you leave with
- A working AI employee on one real workflow, running on your systems, not a demo.
- Its accuracy and cost per outcome, measured on your data against the manual baseline.
- The eval set and human-review design that keep it honest as it runs.
- A ranked map of the next few workflows worth automating, discovered by watching the work.
- An honest recommendation: expand it, or stop here. Either is a fair outcome.
2 weeks · fixed fee
Read-only to start. A working agent, or an honest no.
Nothing changes in your systems without your sign-off. Scoped access, an audit trail, and a human on the calls that matter, from day one.
Why a pod, not a proposal
You discover the work by doing it, not diagramming it.
The workflow is the unit
Automating one task saves minutes. Redesigning the workflow around AI removes handoffs, approvals and legacy tooling, and that is where hours turn into a different way of operating.
Forward-deployed is how AI lands
Embedding an engineer with the domain expert is the model the frontier labs and the best consultancies now use to get AI into production. We bring it to teams that cannot wait in an enterprise queue.
Buy beats build
MIT found AI reaches production about twice as often when bought or partnered as when built in-house. A pod is the fastest, lowest-risk way to buy the outcome and keep the option to build later.
Where it fits
Any function that runs on a repeatable workflow.
Support and customer operations
Ticket resolution, intake and triage, with humans on the edge cases.
Finance and back office
Reconciliation, pacing reports, capital allocation, reporting that used to take days.
Documents and research
Contracts, claims and reports read in minutes, with every conclusion traced to its source.
Go-to-market and revenue
Lead scoring the team believes, content pipelines, the copilot the roadmap keeps deferring.
Engineering and QA
Test generation and change-aware quality, our everylayer product, when the workflow is the codebase.
Operations and scheduling
The repetitive, high-volume coordination work that quietly runs on spreadsheets and goodwill.
Bring the workflow everyone says is too messy to automate.
Two weeks from now you have a working agent on it, or a clear reason not to. Either way you learn more than a quarter of meetings would tell you.