AI reads the email — body, PDF, DOC, XLSX, image attachments and even PMS portal links — extracts hotel, rooms and dates, matches your inventory, and commits to your back office. In seconds, not hours.
2-5 min
Average human time per stop-sale email — read, find hotel, find room, type dates, commit.
20,000+
Stop-sale emails a mid-size tour operator receives per year.
1 in 50
Manual entries contain a data-entry error: wrong dates, wrong room, wrong hotel.
Email Parsing
Ingests .eml plus PDF / DOC / XLSX / image attachments and PMS portal links from configured mailboxes.
AI Extraction
Joint Body & Visual processing using an AI.
Three-Layer Match
Fuzzy Logic + Machine Learning + LLM Disambiguation for 100% ID mapping.
API Integration
Deep native handoff to your backoffice.
Structured Output
Validated JSON, Webhooks, or Direct Commits.
We plug straight into the native REST API — hotels, rooms, allotments. Stop-sales commit themselves.
Not on VoyagerX or xCore? You're still covered. We extract and match against your own hotel inventory, then hand back structured JSON or push it straight to your webhook — ready to land in any backoffice.
Body text, subject lines, PDF / DOC / XLSX attachments, scanned calendar screenshots — even follows links into PMS portals. AI vision reads them all.
Fuzzy Logic catches the easy 80%. Machine Learning handles the gray area. Ambiguous hotel names fall through to an LLM with location metadata.
Every change rendered as before / after / diff. See the exact dates about to be stopped, room by room, in one glance.
Every extraction, every edit, every commit logged with user, timestamp and LLM call. Pass auditors at 9am Monday.
One platform, isolated tenants. Per-tenant API credentials, per-tenant LLM keys, per-tenant dictionaries.
VoyagerX or xCore? Auto-commits via JSON REST. Other stack? Webhook or JSON export — bring your own inventory.
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