This is what a company looks like when the operations run themselves.
Maple Payments is a complete, production-scale company. Below, JieGou's governed AI works its support operations — every number is computed live from the data, with receipts, and every action stops at a human approval gate. Pick a scene.
The setup. Maple's MSA commits to first-response times by account tier — Enterprise 30 minutes, Growth 2 hours, Starter carries no formal SLA. Somewhere in open P1 tickets, commitments are being missed. Finding them means joining tickets to accounts to contract terms — the kind of cross-system check that quietly doesn't happen.
joins CRM tickets ↔ accounts ↔ MSA tier terms
The catch. Each row carries its receipt — the clock math from the data, not an assertion.
The gate. For every breach, a client-facing first response is already drafted — from their reported problem, their tier's terms, nothing invented. Nothing sends without a human. You're the human — try one:
The receipt. Your decisions just became audit-trail entries — the same trail a compliance review would read:
— your approval decisions will appear here —
You didn't configure any of this. We did.
The sweep, the drafts, the gate, the audit trail — set up and operated by JieGou. Your team only ever sees the green checks and the approval taps.
The other loop. Most tickets don't need escalation — they need a good answer, fast. Here the AI drafts replies grounded in the knowledge base (citations attached), and proposes — never sends. The deflection math only works if the drafts are trustworthy; trustworthy means showing sources.
The receipt. Same audit trail, same gate — drafting is cheap, sending is governed:
— your approval decisions will appear here —
Drafted from your knowledge, sent only by your people.
Knowledge ingestion, draft quality, the propose-only guardrail — operated by JieGou.
Trust is a trail, not a promise. This is thirty days of governed operations on Maple — sweeps that ran, drafts that were approved (and the ones that were rejected), a bulk action the governance policy held because it exceeded the auto-approve limit. Every line answers the question "what did the AI do, and who said yes?"
If it isn't in the trail, it didn't happen.
This is the operating record your auditors, your board, and your own sleep want to exist.
The question no single system can answer. "How much revenue is sitting behind our open P0/P1 tickets — by product area?" The answer lives across three systems: tickets, the product-part hierarchy, and account ARR. Sloppy retrieval drowns in the 32,768 tickets; a governed join filters at the data layer and shows its method.
Answers with methods, not vibes.
Cross-system intelligence over your real systems — Salesforce, Jira, Zendesk, Drive — through governed, read-scoped connections.