Your most capable employee isn't human.
A workforce of one: a single AI colleague who knows your whole business, works every system you do, and talks to everyone in it at once. It runs inside your own cloud, and anything risky waits for a person.
Read the film as text
- Jo Whitfield's inbox: 23 requests waiting behind one person, from “Board pack: why is gross margin down 3 points?” to “Can we extend the Q3 discount for Hartwell?”
- Meet Jarvis: one AI employee, with one identity and one memory, working in Slack, Email, Voice and Your app. 38 conversations are open at once.
- Jo asks in Slack, #finance: “Gross margin's down 3 points this quarter. Can you find out why before the board pack?” Jarvis replies: “On it. I'll check every Q3 invoice line against what each customer signed.”
- It reads 4 sources: NetSuite (18,412 Q3 invoice lines), Salesforce (312 active contracts), Contract PDFs (296 price-increase clauses read) and Price list (3 changes since April). From memory: legacy SKUs sit outside the main price list.
- It checks all 18,412 invoice lines against the price each customer signed for: 1,284 lines, across 41 accounts, have been billed below contract since 1 July.
- It replies in the thread with a report attached: gross margin fell 3 points, from 41.2% to 38.2%. Price rises not applied account for 1.9 points, £386k a year; freight costs 0.7 and product mix 0.4. The accounts with most at stake: Hartwell Ltd (£48k a year), Calder & Moss (£37k a year) and Brightwater Foods (£31k a year).
- Anything risky waits for a person. “Re-bill 41 accounts at contract price” is held until J. Whitfield approves it at 16:05; then: corrections queued, 41 notices sent.
- Run QH-2207 is kept as a record: 11 steps, 8m 56s of machine time against an estimated 3 weeks by hand, £4.80 of model spend, replayable and reversible.
- Routines start work with nobody asking: before each invoice run → check every line; offer signed → new starter; invoice 30 days overdue → chase that fits. Once proven, the invoice check runs as a reflex: 3m 12s on run 1, 38s by run 12, with no model in the loop, and it still stops at the gate.
- It runs inside your AWS account (eu-west-2, Your KMS keys, Your VPC), with Jarvis and its tools inside the boundary. QuasiHuman runs the fleet, and holds no keys.
- The film ends on the QuasiHuman wordmark: Your most capable employee isn't human.
Works in the systems you already run
- Salesforce
- SAP
- NetSuite
- ServiceNow
- Zendesk
- Jira
- Workday
- Microsoft 365
- Google Workspace
- Slack
- GitHub
- Stripe
Plus any MCP server or private API. Systems we connect to, not customers.
Your best person is a queue.
Being good at everything is exactly what makes them the bottleneck. Every question, exception and approval (23 waiting) waits behind the same person, in every inbox they own.
What if it didn't?
Meet Jarvis .
One AI employee for the whole business: one identity and one memory, on every desk and in every inbox at once. Name it whatever your team decides.
Works every system
It opens the account, checks what shipped, reads the contract and answers, in one conversation.
Builds the interface
When a paragraph won't do, it builds the form, the approval or the queue, on live data.
Remembers the work
Workflows resume mid-task. What one person teaches it, the company keeps.
Runs in your cloud
The whole stack runs in your AWS account, in your region, with your keys. We never hold them.
Keeps the record
Every action logged with who asked and who approved. Replayable, reversible, stoppable.
Starts in three phases
A landing zone, a boundary around one job, then supervised work you widen as it earns trust.
Ask it where you already work.
Slack, Teams, email, a phone call, or a widget inside your own product. Same colleague, same memory, whichever door someone uses.
Every surfaceSlack and Teams
It answers in the thread, and asks for approvals with buttons in the channel.
Email and phone
It reads the inbox it's given, writes back, and takes calls on a real number.
Inside your product
A widget in your own app that sees the page your user is on and works on it with them.
One question, every system.
Your systems arrive as tools it has been granted. It reads what it needs across all of them, checks the detail a person wouldn't have time to, and answers once.
Cross-system reasoningReads before it writes
Four sources read, one change proposed. Nothing is written until the right person agrees.
Builds the report, not just the answer
When a paragraph won't do, it builds the chart, the table or the approval, and you keep it.
Remembers last quarter
What it learned last quarter, it uses in this one. Someone leaves; what they taught it stays.
It works when nobody's asking.
A webhook, an event or a schedule starts the job with no one in the room. It stops at the same gate and leaves the same record as when someone asks.
Custom routinesInvoice check
Before every invoice run, each line is checked against its contract. Anything below it is held, not sent.
New starter
Offer signed. Accounts, kit, licences and payroll, all open and correct before day one.
Overdue invoice
Thirty days past due. It checks what shipped, what's disputed, and sends the chase that fits.
Nothing happens off the record.
Every action is logged with the system, the decision, who asked and who approved. Anything above the threshold you set waits for a named person.
Controls and auditWaits for a named person
Above a blast-radius threshold, it stops and asks someone you chose. Not a role. A person.
Replay and reverse
Step back through any run, see what it saw, and undo what it changed.
Stop it mid-flight
Cost, time and step ceilings on every run, and a kill switch that stops a workflow where it stands.
The keys stay yours.
Every team that buys software like this asks the same three questions. These are the answers.
- When security asks where the data lives The deployment boundary
- Inside your AWS account, in your region, encrypted with your KMS keys. We deploy and run it through a capped, federated role with no long-lived keys.
- When finance asks what it costs Both models, with the arithmetic
- Per deployment and the work it does, not per seat. $2,999 a month in an AWS account you own, plus 5% of metered model spend, or $6,999 a month in one we run for you.
- When someone asks who did that All six controls
- Every line of the record names the system, the decision, who asked and who approved. Replay it, reverse it, or stop a run where it stands.
Your account. Your keys. Your audit trail.
From a clean AWS account to real work.
Three phases, not a transformation programme. No rip-and-replace and no consultants in your conference room.
- Phase 1
Landing zone
- You provide a clean AWS account
- A capped, federated role with no long-lived keys
- We deploy the whole stack inside your boundary
- Phase 2
Capability boundary
- We map the job that's actually being done
- Your systems exposed as curated, task-level tools
- Every change classified by blast radius and gated
- Phase 3
Supervised operation
- Live behind an approval queue
- Every run logged, bounded and reversible
- Autonomy widened as the evidence earns it
Questions people ask first.
The short answers. Each one links to the page that goes deeper.
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 fullWhat 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 controlsWhere 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 surfaceDoes 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. And once it has proven itself it runs as a reflex on Cerebellum, the runtime we built for it: the same calls with no model in the loop, each one still checked against policy — so it gets faster at your work without ever getting more powerful.
How a skill becomes a reflexHow is it priced?
Per deployment and the work it does — not per seat. Two models. Run it in an AWS account you own for $2,999 a month plus 5% of metered model spend, bringing your own provider keys and paying your own cloud bill directly. Or have us provision and manage a dedicated account for $6,999 a month, with AWS, model spend and third-party services passed through at cost plus a 10% processing fee. Same product either way — seats are how we handle identity and approval, not how we bill you.
Both models, with the arithmeticWhat 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 likeWe'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.