Your most capable employee isn't human.
A workforce of one: a single colleague who knows your whole business, works every system you do, and talks to everyone in it at once — inside your own cloud, never dropping the thread.
When you close the laptop, we open it.
End of shift.
Shift continues.
Your best person is a queue. This one isn't.
Being good at everything is exactly what makes your best human employee a bottleneck. Every question, every exception, every approval waits behind the same person.
A QuasiHuman colleague has no queue. One identity, one memory, one set of granted tools — on every desk and in every inbox at once. Not a fleet of bots to route between.
It answers where your team already is. Then it works when nobody's asking.
- NEW STARTER
Offer signed. Accounts, kit, licences and payroll — all open and correct before day one.
WorkdayServiceNowMicrosoft 365 - OVERDUE INVOICE
Thirty days past due. It checks what shipped, what's disputed, and sends the chase that fits.
NetSuiteSalesforceEmail - TICKET → CHANGE
A bug report becomes a change request, still linked to the customer who raised it.
ZendeskJiraGitHub - MONTH-END CLOSE
Every line matched. The dozen that aren't arrive as a queue, not a spreadsheet.
NetSuiteSAPBank feeds
One thread, wherever the person already was — the widget inside your own product, or the channel they never leave. Same memory, same tools, same approval.
A webhook, a schedule, an event in a system you run: work starts with no one in the room, and stops at the same gate — the one Jo just approved.
- 09:14:02 TRIGGER Offer accepted — R. Adeyemi, Field Ops, starts Monday
- 09:14:03 TOOL Workday · role, manager, cost centre
- 09:14:05 TOOL Microsoft 365 · account and mailbox created
- 09:14:08 TOOL ServiceNow · laptop and phone requested
- 09:14:10 MEMORY Field Ops starters also need the depot app
- 09:14:11 GATE Depot app grants site access — held for approval
- 09:31:20 HUMAN Approved by J. Whitfield · Field Ops
- 09:31:21 WRITE Access granted · induction booked · manager notified
Which system, what was decided, who approved it, what it cost. A transcript is not a record.
Not a chatbot. Six things a chat window can't do.
One colleague, every system.
It opens the account, checks what shipped, reads the contract and answers — one conversation, carrying what it learned between each step.
It builds the interface, not just the answer.
When a paragraph won't do, it builds the form, the approval or the triage queue inside the conversation — and you keep it, on live data.
It remembers Tuesday.
Workflows that resume mid-task, three kinds of memory, a knowledge base built out of the work. Someone leaves; what they taught it stays.
Runs in your cloud, not ours.
The whole stack deploys inside your AWS account, your region, your KMS keys. We run the fleet. We never hold the keys.
Nothing happens off the record.
Every action logged with provenance, replayable, reversible and stoppable. Anything above a blast-radius threshold waits for a named human.
Three phases, not a transformation programme.
A landing zone in your account, a capability boundary around the job being done, then supervised operation you widen as evidence earns it.
It doesn't integrate with your stack. It is given tools, and the keys stay yours.
Every system arrives as MCP — the open protocol for handing an agent a tool. A connector we ship, a server you already run, or a private API nobody has ever connected.
One account, connected once.
An admin connects the system; every colleague session can use it.
- The credential belongs to the business, not a person
- The audit line still names whoever asked
Or your own, just for you.
Someone links their own login. Only they and their routines can reach it.
- Acts with their permissions, never above them
- Revoke the person, revoke the tool
It uses the screen.
Vendor portals and internal apps: it works the page like a person would.
Every surfaceStay in your account.
Credentials sit in your secrets manager, under your keys. We never hold them.
The deployment boundaryTools, not databases.
Task-level tools only — and what they return is quarantined before the model reads it.
Controls and auditThe questions your security team is going to ask anyway.
How does a deployment actually work?
Three phases. Landing zone — you provide an AWS account and a capped, OIDC-federated role with no long-lived keys, and we deploy the full agent stack inside your boundary. Capability boundary — we map the job that's actually being done and expose the systems behind it as curated, task-level tools. Supervised operation — agents go live behind an approval queue, and you widen autonomy as the evidence earns it.
The three phases in full →What stops an agent doing something stupid?
Four independent things. Mutations above a blast-radius threshold stop and wait for a named human. Token, cost, wall-clock and tool-iteration ceilings bound every run, with circuit breakers on repeated failure and a kill switch that stops a workflow mid-flight. An agent can only do what its granted tools allow — no ambient database access, no standing admin session. And everything your systems return is quarantined as untrusted input before the model reads it.
All six controls →Where do agents show up?
An embedded widget in your own applications, Slack, voice and inbound telephony, email, and co-browse — where the agent perceives the page your user is on and acts on it with them. Work also starts without a person in the room: any upstream service, webhook or cron can begin a run. Same memory, same tools, same supervision, whichever door it comes through.
Every surface →Does it get better over time, or just stay the same?
It compounds. Memories are promoted, superseded and retired rather than piled up. A procedure that keeps working is kept as a skill and scored each time it's applied. A tool-chain that recurringly succeeds is promoted into a named, reusable capability — sugar over existing tools, never a privilege escalation, so it gets faster at your work without ever getting more powerful.
How the brain works →How is it priced?
Per deployment and the work it does — not per seat. Seats are how we handle identity and approval, not how we bill you, so putting the whole team behind one colleague costs the same as putting one person there. The stack runs in your own AWS account, so the infrastructure bill is yours, itemised by your own provider rather than marked up by us, and model spend is metered per run and shown against the work that incurred it.
Cost transparency →What do you need from us to start?
A clean AWS account and a capped, federated role; one operational workflow that genuinely hurts; and access to the people who actually do that work. That last one matters most — the job in the org chart and the job being done are rarely the same thing.
What the exchange looks like →We're taking a small number of design partners into the first cohort.
A handful of enterprise teams, engineered with rather than sold to. If your landscape is messy and the bottleneck costs real money, we should talk.