Operational load lives in inboxes and in people's heads: requests land everywhere, the real procedure is tribal knowledge, and SLA breaches surface as complaints. An AI-native operations function puts the whole desk on a workforce that intakes every request, runs your written SOP, and holds every payment and commitment for the ops lead.
Installed in about 35 min. Governed and audited from the first run.
Every stage of the motion is worked by the right kind of worker (an unattended play, a room of specialists for the hard cases), all reading and writing one shared record, with your ops lead at the gates that matter.
You don't wire this together or choose an architecture. You describe the outcome; the workforce brings the right kind of worker to each part of the motion, and they all work from one shared record, under one set of rules.
The repeatable requests (intake, categorize, match the SOP, run the routine steps) go start to finish on their own, unattended, the moment they land, day or night.
When one request touches several systems at once (verify a contract, update a record, prepare a payment), a room of specialists assembles the work together, each owning its part.
You don't operate a queue. You talk to one coworker that runs intake, remembers your standing instructions and thresholds, and surfaces only the exceptions that need you.
Every worker reads and writes the same requests, SOPs and SLA clocks, and every one obeys the same gates, budgets and audit. No worker is ever off the leash.
The procedure stops living in people's heads. Your runbooks are retrieved and cited on every run, so the SOP is the thing that executes, and every step it takes lands in the audit trail.
There's no integration project and no architecture to pick. You delegate the outcome, the workforce assembles the right workers, and every one of them lands under the same gates and audit.
Not another intake form: a workforce that owns the repeatable majority of requests end to end, runs your written SOP on every one, and hands your people the exceptions and the judgment. The week shifts like this (a worked scenario, not a benchmark):
Email, chat, form: each request is categorized, matched to the written procedure that governs it, and stamped with an SLA clock the minute it lands. Nothing lives in whichever inbox it happened to hit.
Routine steps run from your own knowledge base, retrieved and cited on every run. Where the procedure doesn't cover it, the request escalates to a person instead of being improvised.
Vendor payments, external commitments, anything the SOP marks as judgment, all hold at the approval gate with the relevant SOP section attached as evidence, waiting for the ops lead.
Every open request is checked against its clock continuously, so drift surfaces as an escalation with runway, not as a customer complaint, and cost per closed request is priced from real run spend.
Otto isn't a dashboard you operate, it's the coworker you delegate intake to. The night's requests, what's still waiting on a vendor, the payment that's ready: it arrives summarized, the pieces that need you are already held with the SOP section as evidence, and each approval releases exactly one thing.
Every message, tool call and approval in this exchange lands in the audit trail. How the gates work →
A scenario: a shared-services ops team with SLA targets on every request type. These are real screens, not mockups.
Every executed step is retrieved from your SOP knowledge base and cites the passage it followed; what the runbook doesn't cover escalates to a human instead of being improvised. Inbound request text is untrusted, so a prompt-injection shield screens it before it reaches a model. The control plane →
Payments, external commitments and the exceptions your SOP flags all stop at the gate, enforced at the tool call, at runtime. Each approval is single-use: one grant, one action, 24-hour expiry, and delegation covers the days the ops lead is out. How approvals work →
Every intake, executed step, hold and decision lands in the audit trail, pinned to the config version that ran, SLA evidence included. Every run's spend rolls up under budgets with hard caps, so cost per closed request is a number you read, not estimate.
