Approval boundaries
Humans approve external action until the pilot evidence supports a narrower delegated boundary.

AI workforce / Travel operations
Gappy coordinates guide and supplier confirmations, follows up on missing replies, and checks the latest booking before work is called complete.
Watch the 60-second prototype walkthrough
Tomorrow's departures
One fixed, fictional workflow. Follow the same booking through approval, guide response, verification, a material change, and reconfirmation.
Interactive prototype · Sample data · No messages sent
Fixed sample instruction
Check tomorrow's departures and follow up on unconfirmed guides.
Illustrative queue overview · static sample snapshot
Observable activity & evidence
Run the sample to inspect tomorrow's queue.
Current work item · Booking #2871
Tomorrow · 09:00 JST · Version 3 · Guide Tanaka
Current status
READY TO RUN
No valid confirmation exists for 09:00 · v3.
Mt. Fuji Day Tour
Booking #2871 · Version 3
Before
Find task → contact → wait → check → chase → update → check againWith Gappy
Detect → prepare → approve → follow up → verify → hand off02 / Product walkthrough
The short film follows one sample booking from instruction and approval through a guide response and current-booking verification.
This second projection stays synchronized with the interactive stage above: one booking, one lifecycle, and one current source of truth.
Current status
READY TO RUN
No valid confirmation exists for 09:00 · v3.
Mt. Fuji Day Tour
Booking #2871 · Version 3
04 / The operating layer
Gappy works around the operation already in place. Pilot scope, connection method, decision boundaries, and evidence requirements are agreed with each operator.
Map one workflowHumans approve external action until the pilot evidence supports a narrower delegated boundary.
Unknown, stale, conflicting, or missing evidence stays open or moves to a person.
A reply is compared with the latest booking snapshot before confirmation is marked complete.
Partial / pilot-ready foundation
Gappy can be configured around each operator's brand, workflow rules, and operating context while the underlying AI Workforce runtime stays consistent.
Configured with your team during pilot.Guide confirmation
05 / Workflow states
Three views of the same fictional booking. These are interactive workflow examples, not customer stories or deployed-results claims.

Surface the booking, prepare the request, and keep operator approval visible.

Keep the work open, follow up inside a bounded rule, and hand off at the deadline.

Invalidate the old 09:00 confirmation when the current start time becomes 10:30.
06 / Business impact
Model the human work that could be returned to your team. Gappy measures completion, intervention, and elapsed time—not activity volume alone.
No Gappy performance benchmark is shown because real operational measurement has not begun.
Illustrative capacity model
Illustrative estimate based on your inputs. Not a guarantee of realized savings.
07 / Start bounded
A workflow review can lead to offline evaluation, Shadow Mode, and human-approved execution. Scope and timing are discussed on the demo call.
Book a demoMap the workflow, systems, deadlines, and exceptions.
Compare Gappy against historical operational cases.
Process real cases without sending externally.
Let operators approve each external action.
MEASURES TO DEFINE TOGETHER · NO BENCHMARK IMPLIED
08 / Before we start
No. The pilot is designed around the existing source of booking context. Connection method and access are agreed per operator.
The first workflow is scoped to assigned guides or suppliers. The operator decides the approved recipient and communication boundary during pilot setup.
Not by itself. The current booking details and available evidence must be checked first. Unknown or conflicting state stays open or moves to a person.
Start in offline or Shadow Mode, then use human approval. Responsibility expands only where the agreed operating evidence supports it.
A partial multi-tenant and white-label foundation exists. Supported brand and workflow configuration is completed with your team during pilot; it is not self-serve SaaS onboarding.
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