From color-coded chaos to clean, bookable data, WWStay’s AI reads any manpower sheet, roster, spreadsheet, or format, converting it into ready-to-book housing in minutes with 95% faster processing and 70% fewer errors.
Managing complex workforce logistics via manual spreadsheets has created a costly gap between live staffing changes and accommodation needs. This fragmented process forces coordinators to continuously interpret volatile data, resulting in missed bookings, compliance failures, and inefficient resource allocation. Consequently, the firm faces significant operational churn as static housing workflows cannot adapt to the speed of modern project deployment.
WWStay bridged the gap between operations and accommodation by deploying an AI-driven engine that integrates directly with the client’s existing workflows. Instead of forcing a process change, the platform was trained to read the client’s "live" complex workbooks—instantly decoding dense color codes, inconsistent formatting, and merged headers to apply intricate rooming rules automatically. The system further synchronized the workflow by capturing updates directly from emails and offering flexible intake options like chat and copy-paste. This transformed a static, error-prone manual process into a dynamic, real-time stream where housing stays perfectly aligned with the shifting manpower plan.
⏱️ Processing Time: 5–15 Minutes(85–95% Reduction in Time)
🎯 Error Rate: 2–5% (60–75% Drop in Errors)
📉 Workflow: Synchronized & Live (Real-time Availability)
Smart ingestion & normalization: Automatic intake resolves merged cells, inconsistent tabs, and free-text into a unified schema.There were also other options for requirement intake apart from uploading a file. Copy paste a preferred format in chat, request form and free form chat to just enter all details. Even changes made via email were captured and integrated. (consider a flowchart visual for the intake points one below the other or other icons)
Policy-aware parsing: Applies customer rules (e.g., single rooms for supervisors, shared rooms for crew), validates dates, de-duplicates travelers, and catches conflicts.
Ready-to-book outputs: Generates a structured booking queue with flags and audit trails, enabling fast approvals and smoother handoffs to suppliers.
The result was a data-driven, self-correcting housing engine that processed rosters faster, more accurately, and at a fraction of the administrative effort. This automation created measurable impact in project cost control and operational efficiency across all field sites. (keep this portion as is and the below points in grids on the left and the arrow diagram on the right.)
