Client case study · WhatsApp AI Assistance
How Provecta put AI assistance on a property developer's own WhatsApp number to catch every ad-driven buyer, qualify them instantly, and log them straight into the CRM — so no enquiry goes cold.
The challenge
The developer markets its residential project with paid social campaigns that push buyers to WhatsApp. The problem wasn't demand — it was response.
Enquiries arrived faster than the sales team could answer, and every evening, weekend and campaign spike became a dead zone where buyers waited hours for a reply or moved on to the next advertiser. There was no consistent record of who had asked what. For a developer selling its own units, each ad-paid enquiry left waiting was a potential sale walking away.
What Provecta built
AI assistance, powered by Claude on bRRAIn, that operates directly on the developer's own WhatsApp Business line. The developer keeps its number and its data; Provecta builds and runs it as its service provider, with a human always one message away.
In the buyer's language — no more overnight or weekend gaps.
Location, unit types, payment plans, availability, next steps (pricing shared on request).
Budget, timeline and which unit they're after — through natural conversation.
With a full touchpoint log, so nothing lives lost in someone's chat history.
AI assistance handles the repetitive work; people close the deal.
How it works
Messages the WhatsApp line from an ad
Answers & qualifies in seconds
Lead written to Zoho with full history
Ready buyers handed to a human rep
The results
It runs at a fraction of the cost of an after-hours customer-service hire — and unlike a new hire, it answers every buyer at once, never takes a night off, and keeps a clean record of every conversation.
Your WhatsApp line should answer every buyer like your best salesperson — instantly, day or night. See it work on your business.
Put it on your line →Case study prepared by Provecta. The project is a residential development in Mombasa; pricing is shared on request and any returns referenced in the developer's own materials are projected, not guaranteed. Reply-rate figure reflects the developer's observed inbound performance after deployment versus human-only handling.