High-Volume WhatsApp Support Center (Gaming & Player-to-Player Payment Brokerage)
High-volume customer service — a WhatsApp support center for online games and player-to-player payment brokerage
The client described a 30-person center handling thousands of WhatsApp inquiries a day on an unofficial solution with repeated blocks and severe lag. The delivered project moved the operation to official WhatsApp Business API infrastructure with returning-customer recognition, structured matching and inquiry classification; the starting-volume and latency figures are client-reported, not post-launch measurements.
The Challenge
According to the client's discovery account, the center handled thousands of WhatsApp inquiries a day with 30 employees across three shifts and manually matched deposit and withdrawal requests in a spreadsheet. The client also reported repeated blocks on an unofficial WhatsApp solution, about five seconds of lag per character and non-clickable phone numbers. These figures describe the client-reported starting point and were not remeasured in the project system. The verifiable implementation need was a move to official WhatsApp Business API infrastructure and a documented operating flow.
The Solution
An entire support center was built on official WhatsApp Business API infrastructure — two separate systems, an instance per project, connected to Meta Business Manager with separate permissions and load tests that verify it handles thousands of inquiries a day. On top of the infrastructure, five components were built, each tailored to an explicit requirement from the conversation: (1) a short 2-3 step opening menu that summarizes the request and immediately routes it to an agent by inquiry type, with an absolute focus on response speed to get close to a regular WhatsApp experience; (2) returning-customer recognition — on the first inquiry the details are saved, and from the second inquiry the customer skips the bot and goes straight to an agent with all the saved context; (3) a dedicated SQL database into which the data from Excel and the previous system was imported and cleaned, used to match withdrawers to depositors by amount and for fast search under high volume; (4) an agent interface for 10 agents across 3 shifts with label-based filtering, canned responses and clickable phone numbers; (5) an AI layer that identifies in real time whether the inquiry is a withdrawal, a deposit or a technical complaint — tags it accordingly, and sends technical complaints as an automatic alert to Slack. All of this was delivered on a fast-delivery track with close coordination through handover.
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