Process automation
Your team's weekly flow has two kinds of steps: the ones that need judgment, and the ones that just shuttle data: categorizing tickets, moving them between Kanban columns, kicking off downstream actions, drafting boilerplate responses. The judgment steps are why your team was hired. The mechanical steps are where AI fits, once you know how to find them.
We use this internally and ship it for clients. Our own issue tracker is augmented with AI that classifies new issues by type, priority, and team, and that takes action when items move between Kanban columns: notifying the right people, updating related items, drafting the right summary. The methodology travels to any team's process: find the mechanical steps in the flow, ship narrow AI components for each, leave the judgment-heavy steps to humans. Start small, watch it work, expand.
Capabilities
We sit with the team and map every step in the flow: who does what, with what input, producing what output. The map shows where humans add real judgment and where they're shuttling data between systems. Only the shuttle work is a candidate for automation; the judgment work stays where it belongs.
Each mechanical step gets its own narrow AI component: purpose-built, observable, easy to reason about. Not one giant “do my whole job” AI. Many small, replaceable pieces, each one shipping and improving independently.
Between automated steps, humans review the AI's output and approve it before the next step runs. Errors get caught early; the process stays auditable end-to-end.
New AI components start in shadow mode: running alongside humans, comparing outputs, but not taking action. Once the AI matches human output for two weeks, the human review becomes optional. Trust earned, not assumed.