A store is a handful of judgment calls wrapped in a mountain of repeatable work: order questions, returns, refunds, catalog upkeep. An AI-native store functionputs the whole back office on a workforce that runs it around the clock, while every refund over your threshold and every storefront change still waits for you.
Installed in about 20 min. Governed and audited from the first run.
Every stage of the store's back office is worked by the right kind of worker (an unattended play, a team that faces your customers, a room of specialists), all reading and writing one shared record, with you 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 store, and they all work from one shared record, under one set of rules.
The repeatable plays (process an in-policy return, draft a product description) run start to finish on their own, unattended, overnight.
Order questions are met in the moment by a team that answers from your live order data and your published policies, cited, and escalates the second it isn't sure.
When a catalog change needs the copy, the pricing and the stock reconciled at once, a room of specialists assembles it together, each owning its part.
You don't operate a dashboard. You talk to one coworker that runs the back office, remembers your standing instructions, and brings you only the calls that touch margin.
Every worker reads and writes the same orders, returns and catalog, and every one obeys the same gates, budgets and audit. No worker is ever off the leash.
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.
Run the math: a queue that never sleeps and a returns policy nobody has time to apply consistently is where margin quietly leaks. The workforce owns the repeatable order work continuously and hands you the calls that are genuinely yours. The week shifts like this (a worked scenario, not a benchmark):
“Where is my order?” at 1 AM gets an answer at 1 AM: the actual order looked up, the carrier status pulled, the reply grounded in your published shipping policy and cited. Never improvised, because an invented policy in a customer inbox is a real cost.
Returns that sit inside your published rules are checked and processed without waking anyone. The eligible ones move; the exceptions escalate. Volume stops being a headcount problem.
Any refund over your threshold always stops at the gate, margin protected structurally, not by whoever is tired at midnight. One approval releases exactly one refund.
Volume scales; headcount doesn't have to. Budgets with hard caps keep run spend bounded, and cost per resolved order-issue is a number in the morning briefing, priced from real run spend.
Coral isn't a dashboard you operate, it's the coworker you delegate the back office to. The night's order questions, the returns processed, the one refund that needs you: it arrives summarized, the pieces that touch margin are already queued with their 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 DTC brand with a $150 refund auto-approve threshold and a published returns policy. These are real screens, not mockups.
Customer answers are drafted from your shipping and returns policies, retrieved and cited; what the docs can't ground escalates to a human. Inbound customer text is untrusted input, so a prompt-injection shield screens it before it reaches a model. The control plane →
Refunds above your threshold and every catalog change hold at the approval gate, enforced at the tool call, at runtime. Each approval is single-use: one grant, one refund, 24-hour expiry, then the next one asks again. How approvals work →
Every lookup, reply, refund and catalog draft lands in the audit trail, pinned to the config version that ran, and every run's spend rolls up under budgets with hard caps, so cost per resolved order-issue is a number you read, not estimate.
