The Company
Watts Corporation is a global leader in piping, flow control, and water quality solutions with a dedicated India operations team based in Pune. Their India division supplies specialised brass, copper, and stainless steel fittings to public infrastructure projects, municipal water systems, and industrial clients across 22 states.
The Challenge
India's public procurement ecosystem generates thousands of plumbing and piping tenders every month, but Watts Corporation faced a unique filtering problem:
- Material specificity: 90% of piping tenders specify generic materials (GI, HDPE, PVC) that Watts doesn't supply. Finding the 10% that specify brass, copper, or stainless steel required reading every tender document
- Geographic spread: Relevant tenders appeared across 22 state portals, dozens of municipal sites, CPPP, and GeM,impossible to manually monitor
- Specification buried in BOQs: Material specifications were rarely in tender titles. They were hidden in Bill of Quantities tables on page 40 or 80 of RFP documents
- Low conversion from noise: The BD team was spending 80% of their time filtering irrelevant tenders and only 20% on actual bid preparation
The Solution
Material-Aware AI Scouting
Bidovate's AI was trained on Watts Corporation's product catalogue, including specific alloy grades, pipe diameters, pressure ratings, and fitting types. The system could distinguish between a generic plumbing tender and one that specifically required brass ball valves or copper press fittings.
Pan-India Portal Coverage
The platform monitored 1000+ procurement portals across all 38 states and 9 union territories plus central platforms, providing genuine pan-India coverage. Tenders were auto-classified by material type, allowing Watts to see only brass, copper, and stainless steel opportunities.
BOQ-Level Parsing
Document Intelligence extracted material specifications from BOQ tables deep within RFP documents, matching line items against Watts Corporation's product range and flagging compatible opportunities with quantity estimates.
Results
Over 10 months:
| Metric | Before | After | Change |
|---|---|---|---|
| Tenders screened | 200/month | 2,400/month | 12x increase |
| Material match accuracy | ~70% (manual) | 99.8% | Near-perfect |
| States covered | 8 | 22 | 90% of India |
| BD team time on screening | 80% | 15% | Freed for strategy |
| Qualified opportunities found | 8/month | 35/month | 4.4x increase |
| Revenue from govt tenders | Baseline | +180% | Strong growth |
Key Takeaway
For specialised manufacturers, the challenge isn't finding tenders,it's finding the right tenders in a sea of irrelevant ones. Material-aware AI scouting turned what was a needle-in-a-haystack problem into a structured, automated pipeline.
"Before Bidovate, we had two people doing nothing but reading BOQs all day. Now the AI filters 2,400 tenders and shows us only the 35 that match our materials. It's a different business.",India BD Lead, Watts Corporation
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