How Salmon resolved entities, mapped parent-child hierarchies, and deduplicated a 500K-record CRM for a leading real estate technology platform.
Years of acquisitions and CRM migrations left a major real estate technology company with over half a million account records — and no one trusted any of them. The same client appeared as six different accounts: "Cushman & Wakefield (HQ)," "C&W - Brisbane," "Cushman Wakefield Commercial Ireland Ltd." No parent-child linkage. Stale firmographics. Reps Googled everything anyway.
Pipeline reviews turned into arguments about which account was the "real" one.
Global clients appeared as dozens of disconnected records — one per office, one per legacy system, one per acquisition. No parent-child mapping. No way to see total relationship value across the same organization.
Revenue from 2019. Pre-pandemic headcounts. Inconsistent industry classifications. Prior enrichment vendors matched around 40% of accounts — and got abandoned.
Same company, different name variations, different addresses — sometimes different industries. Marketing hit the same org three times. CRM dedup rules couldn't keep up with the variation.
They'd tried this before. D&B's hierarchy product needed clean DUNS numbers they didn't have. An internal team spent three months manually deduping 20K records before the project stalled. Native CRM dedup caught exact matches and missed everything else.
Traditional dedup tools pattern-match on the fields you already have. When those fields are wrong, matching fails. Salmon researches each account the way an analyst would — then uses that research to resolve entities, build hierarchies, and flag duplicates.
A typical account from the CRM: a company name that's an abbreviation of a well-known enterprise, a stale address, outdated revenue, and no connection to the five other records in the system that represent the same organization.
Salmon's agents search company registries, the corporate website, SEC filings, and LinkedIn to identify this as a subsidiary of a global real estate services firm. The abbreviated name resolves to a known legal entity.
This record is linked to its parent organization and five other accounts in the CRM that represent offices, divisions, or legacy name variations. A single account tree emerges from what looked like unrelated records.
Revenue, headcount, industry classification, website, HQ address, and key contacts are all updated to current values. Stale data from 2019 is replaced with verified 2026 figures. Confidence scores attached to every field.
A resolved, enriched, hierarchically-linked account record. Duplicates flagged for merge. Parent-child relationships mapped. Every field sourced and scored. The ops team can finally trust what's in the CRM.
Three weeks. Pipeline reviews stopped being arguments about data and started being conversations about strategy.
514K accounts resolved into distinct, verified entities. Abbreviations, regional variations, and legacy names mapped to canonical organizations. What took three months manually for 20K records — done across the full CRM in weeks.
Corporate structures mapped — HQ, subsidiaries, regional offices, divisions. For the first time, the team could see total relationship value across an entire organization instead of fragmented deals across disconnected accounts.
Duplicate clusters flagged with merge recommendations. Every surviving account enriched with current firmographics. 40+ fields where most had fewer than 10 before.
The alternative: years of manual cleanup. Their internal team stalled after three months on 20K records. At 514K, you're looking at a project that never finishes — and by the time you're halfway through, the first records are already stale again.
If your CRM has grown through acquisitions, migrations, or years of inconsistent data entry — different industry, same data debt.
Export a few thousand accounts and see what Salmon finds — duplicates, broken hierarchies, stale data. No commitment, no contract — just proof.
kevin@salmonrun.aisalmonrun.ai