House of Waterproofing (HOW) is India's first organised waterproofing brand — and a Skkyee portfolio company. When we took on HOW, the ambition was clear: build the most technologically advanced company in an industry stuck in the 1990s. No standards. No documentation. No digital customer journey. Just phone calls, notebooks, and best-guess scheduling.
This is the story of how we built the AI infrastructure that changed that — and what it means for every service business still running on manual processes.
The Challenge
The waterproofing industry in India has no technology layer. Customer complaints arrive by phone. Diagnostics happen on-site with no prior information. Scheduling is done manually on spreadsheets. Follow-ups depend on individual technicians remembering to call back. Warranty tracking — for 10-year guarantees — lives in filing cabinets.
We set out to do what no waterproofing company had done: build a fully digital, AI-powered customer experience from first contact to decade-long warranty management.
The constraints were real:
- No existing digital infrastructure — no CRM, no ticketing system, no knowledge base
- Field technicians with varying levels of tech literacy
- Customers who expected phone-based service and WhatsApp communication
- Highly variable problem types — leaks, seepage, dampness, each requiring different diagnostic flows
- 10-year warranty obligations requiring long-term tracking and proactive outreach
What We Built
AI Diagnostic Chatbot
The first system was a diagnostic chatbot that customers interact with through WhatsApp and the HOW website. Instead of waiting for a phone callback, customers describe their problem — including photos of the affected area — and the AI performs an initial assessment.
The chatbot asks structured follow-up questions based on the problem type, collects location and property details, and generates a preliminary diagnostic report. By the time a human technician sees the case, they already know what they're dealing with.
Automated Scheduling Engine
The scheduling engine replaced manual spreadsheet coordination. It factors in technician availability, geographic proximity, problem urgency, and required expertise to assign the right technician to the right job at the right time.
Customers receive instant confirmation with their technician's name, photo, and arrival window. No more "someone will call you back."
WhatsApp Agents
Because HOW's customers are primarily WhatsApp-native, we built AI agents that operate entirely within WhatsApp. These agents handle initial inquiries, send appointment reminders, share pre-visit checklists, and collect post-service feedback — all without requiring the customer to download an app or visit a website.
Warranty Tracking System
For a company that offers 10-year warranties, long-term tracking isn't optional — it's existential. The warranty system automatically logs every job, stores diagnostic data and photos, schedules annual check-in reminders, and generates warranty documentation that can be retrieved instantly by customer ID.
The Results
Response time dropped from 48 hours to under 90 seconds. That's not an incremental improvement — it's a category shift. HOW went from a company that called you back the next day to one that responds before you've put your phone down.
Over 500 jobs have been digitally managed through the system, each with complete diagnostic data, scheduling history, and warranty records — all searchable, all accessible.
Zero manual follow-ups. Every appointment reminder, feedback request, and warranty check-in is handled automatically. The operations team focuses on quality and growth instead of chasing callbacks.
10-year warranty tracking is now automated. Every job is logged with full diagnostic history. Annual check-ins are scheduled automatically. Warranty claims can be verified in seconds.
What Made It Work
This wasn't a technology showcase. It worked because we followed the same 90-day deployment process we use across all our deployments:
- Phase 1 (Days 1–30): We mapped all 23 manual touchpoints in the customer journey, audited the existing data (which was mostly in notebooks), and defined success metrics that mattered: response time, digital job completion rate, and zero-touch follow-up rate.
- Phase 2 (Days 31–60): We built the diagnostic chatbot and scheduling engine first — the highest-impact, fastest-to-deploy systems. WhatsApp integration was critical because that's where HOW's customers already communicate.
- Phase 3 (Days 61–90): Staged rollout to 20% of incoming inquiries, then scaled to 100% over three weeks. Warranty system went live in parallel. Knowledge transfer included field technician training sessions.
What's Next
HOW is now exploring predictive maintenance — using historical data from their growing digital job database to identify buildings likely to develop waterproofing issues before they become complaints. That's the power of having clean, structured data from day one.
The system we built isn't just serving customers — it's generating the intelligence that will drive HOW's next phase of growth. Every job creates data. Every data point makes the AI smarter. Every improvement compounds.
In an industry with no technology, the first company to digitise doesn't just gain efficiency — it defines the standard everyone else will have to follow.