AI power for everyone — not the engineers alone. Each person runs it on the tools they already use, under the access they already have. Adoption you can see and measure across the whole org — with central control in security's hands: one log, hard caps, instant stop.
Automation comes with adoption — not as a separate project. As people do their repeated work in amahub, you see the repeats — and turn any of them into an autonomous agent that saves the hours and shows real AI impact. Every person gets this ability automatically.
amahub replaces none of it — it sits on top, under each person's own permissions.
The left one is already happening. The right one takes a sign-in.
When every task runs in one shared place, repeated work surfaces on its own — the same ask, across desks. One click turns it into a shared process for the whole team.
Spend against the cap, every connected system, every gate decision, and the teams that have not started yet — updated as tasks run, not assembled for a quarterly review.
Nobody files a ticket saying "I do this every Tuesday". When all the work runs in one place the repeat shows up by itself — the same ask, three desks, week after week. You see it before anyone mentions it, and one click makes it a shared process.
Claims handlers, payroll clerks, analysts. No script, no model choice, no API key. They type the request; the runtime writes the code, runs it in its own sandbox, checks the result.
That is the whole instruction.
The person sees only what they are allowed to use, already signed in.
A non-technical owner signs it off.
That is the entire onboarding.
Dana in CS. Ticket texts and customer emails never left the runtime.
Everyone works in one place, so the workspace sees the repeat.
No automation team, no ticket to IT. A scheduled run keeps the same full trail as a manual one.
And every runtime sits inside one policy perimeter — vault, data classes, budgets, tripwires, stop:
Keys never leave the vault, a run reaches only the systems that person is already allowed to use, and every step and every cent is on the record. There is no checkbox for an admin to forget — so your auditor gets the same answer every time they ask.
Credentials live in a vault the agent can never read. It can use a key to open a door; it cannot look at the key. There is no debug mode that changes this.
All internet traffic passes through a single supervised gate. Allowed destinations are listed in advance; everything else is refused and written down.
Every action is recorded as it happens — the actual step, timestamped, not a summary written afterwards. Your auditors can read it. So can you.
Any task stops mid-step — its owner, an admin, or an automatic rule can cut it. Stop means the step in progress is cut, not politely finished.
Spending limits per person and per company, set before anyone runs anything. A task that reaches its cap stops and says so. New workspaces start with conservative limits already configured.
No policy language to learn. Write it the way you'd say it — and it unfolds into the real thing: credentials in the vault, approved app connections, allowlists at the gate.
Every connected system carries a label. Policy decides what each class may do — before anything runs.
Deterministic rules watch every session. Critical ones don't alert — they cut the session mid-step, then a person decides.
Slack, Notion, the wiki — each runs under your company's own approved app. The admin signs off once, in the vendor's console.
Live sessions on one screen. A map of where data went. An alert queue routed to Slack, a webhook, or a named admin. A dangerous session is cut automatically — then a person decides.
SOC 2 and ISO 27001 readiness plans map point by point to the controls above — the product passes the audit, not a slide deck. A separate HIPAA plan covers health clients, with model traffic routed under a BAA.
Every repeated task in the company is money leaking on a schedule. In one shared workspace it finally shows: save on everything that repeats — the best runs become buttons, the busiest patterns become the next automation — and you see the saving the moment it lands, not in a quarterly review.
People fear AI — for their jobs, for the data, for looking stupid. Training doesn't fix that. What works is learn by example: one strong person figures it out first, and everyone can open their real runs and see exactly how. Give that person visibility, and adoption spreads on its own.
A good run becomes a button — "Make it a process" turns one success into a team process. Sessions are shared, so people learn from real examples. Six months in, you hold a library of proven processes no competitor starts with.
Who uses it, which teams get value, what it costs — by day, by model, by person, by group, every task to the cent. Shadow AI can never give you this data.
Failed, stopped and unusually expensive sessions are a map of bottlenecks. One person improves a process; the whole team gets the new version. Tried, shared, improved, standard.
Usage data answers the planning question: where people spend the most agent time is where the next big saving sits.
A finished report is published inside the workspace under a name — "weekly-report". A colleague's task picks it up and builds on it: people and AI coworkers pass work to each other without a single file leaving the walls.
Forty seconds, any language. We send the recording exactly as you said it — no transcription, nothing in between. A person here listens to every one and replies within 24 hours.
amahub is itself an MCP server. Connect it to Claude — desktop, web or the terminal — and configure the workspace by asking: connect the tools, put people in teams, grant access, set the spend caps.
Sign in with your own account, approve it on one consent screen. No key to paste and nothing to copy into a config file. Every change it makes is written to the audit ledger with your name on it, and you can revoke the connection from the admin page at any time.