Tyzor® titanates & zirconates. System D.
Regional specialty chemicals. System E.
DK Energy — 2,000+ formulations. System B.
Modified acids. CAD $80M. System F.
Downhole additives. $60M. System C.
$1.6B target. Yet another system.
Sales and operations teams spend hundreds of hours per quarter manually consolidating reports, reconciling customer records, and chasing data across subsidiary systems. Work that should take minutes takes days.
Duplicated systems, overlapping vendor contracts, redundant manual processes — and the cost of maintaining transition teams for every new acquisition. Integration overhead compounds with every deal.
Sales teams can only sell what they can see. Without visibility into what a customer buys across all subsidiaries, cross-sell opportunities go unnoticed. The entire process depends on individual reps manually discovering white space — and at this scale, they can't.
Dashboards require clean, unified data — which doesn't exist yet across these entities. AI handles the messy reality: inconsistent naming, different formats, missing fields. It does the matching humans can't do at scale.
4 acquisitions in 2 years. Italmatch potentially next at $1.6B. The silo problem compounds with every deal. Building this capability now means each future acquisition is easier, not harder.
We specialize in building AI-powered automation for companies with complex, multi-entity data problems. We've seen this pattern before — and we ship working systems in weeks, not quarters.
Buying from 3 subsidiaries. Missing: Downhole Fluids, Modified Acids.
Buying fuel additives only. Match: Production chemicals, Catalysts.
DK Energy customer. No refining additives relationship yet.
Catalysts customer only. High fit for fuel additives + production chemicals.
Workshop with IT + sales from 2 pilot subsidiaries. Map all customer data sources. Extract and securely transfer customer records, transaction history, and product catalogs.
AI matches customer records across subsidiaries — linking different names, addresses, and formats to the same parent company. Joint human review of match quality. Iterative refinement.
Map each unified customer against the full product portfolio. Score cross-sell opportunities by customer size, product fit, and competitive displacement potential. Build the recommendation engine.
Deploy the sales rep dashboard. Present top 50 opportunities to sales leadership. Train account managers on the tool. Measure initial pipeline generated.
Roll out to remaining entities. Add new acquisition data as deals close. Build ongoing intelligence layer with automated refresh and CRM integration.
Customer data exports from 2 pilot subsidiaries (CSV/Excel is fine). A 2-hour workshop with IT and sales stakeholders. A sales leader to review match quality at week 4. No IT integration work required during the pilot.