Design your process once. A team of AI agents works the queues, a human approves anything that leaves the building, and everything the agents learn along the way is kept — so the same process runs a little better every time you use it.
Early access · bring us a real workload and we'll set you up
One agent on one task is a prompt. A team of them on a real process is four jobs: allocate capacity to the right stage, instruct each agent for the step it is on, supervise the ones that stall, and stay accountable for what was done. A system that does only the first is a scheduler.
A central allocator works out which stage each unit of agent capacity should be on, and re-decides as the backlog changes shape. It ships in advisory mode: it publishes the plan and an owner turns enforcement on deliberately. A scheduler nobody has watched does not get to move real work.
Finish what's already in flight before pulling more in. Per-stage work-in-progress limits and an admission throttle keep the pipeline from filling up faster than it empties — Theory of Constraints and Kanban, applied to agents.
Every allocation decision is published with the reasoning behind it, so you can see why each agent is on the stage it's on. It is an explained plan you can audit, not an opaque scheduler you have to trust.
Priority by organization and project, plus knobs for how hard work is pulled along. The policy is yours to set, and the allocator applies it the same way for every agent — once you have armed it.
These aren't mockups — this is WorkflowHub running WorkflowHub's own software delivery, captured from the live product. Seven built-in themes; you're looking at one of the dark ones.
Built on open standards — no proprietary lock-in
Six shapes of office work, one governed platform to run them on — safely, with every step logged.
The AI reads, classifies and drafts replies for every message — a human approves before anything sends.
No more waiting on someone to run a report — the workflow queries it and attaches the answer automatically.
Point a workflow at pages or sources you define; the AI gathers what's there and files it in a usable shape.
The agent drives the application itself — clicking and typing exactly the way a person would. No re-keying.
Drop a PDF, scan or attachment on a queue; AI extraction fills the workflow's fields, ready for a quick check.
Define the fields that matter on your form — the AI populates them from whatever source you point it at.
This is what keeps automation safe: no rogue AI decision ever reaches the outside world unreviewed.
Pulls the next work item, gathers context from enrichment queues, drafts the response and fills the form.
Applies rules and routing, decides the next queue, and prepares the external action — but stops at the boundary.
A person reviews and approves. Only then does the side-effect — send, pay, post, write — actually fire.
Safe, governed automation is the front door. What compounds behind it is institutional memory and process intelligence.
Turn a scatter of $20-a-seat AI subscriptions into one governed enterprise capability everyone works through.
A ticketing system and a central repository of documents and instructions, in one. Ask it for help and it already knows how this was solved before — it learns your process the more you use it.
Who does what, how often, and where things stall — surfaced as real, data-driven process improvement. Ship a productivity report instead of guessing.
Let the 20-year veteran automate their own task. The system learns how they do it — so that knowledge outlives the person.
Every action tracked, every external step human-approved, and your credentials never leave your machine. We're onboarding early-access teams now — bring a real workload.