Agent Account Scoring & Deduplication

Duplicate accounts split your activity and pipeline across records, breaking forecasts and the AI tools your team relies on. Worse, most dedupe tools pick a winner on surface-level fields, so you risk merging away the account that matters most.

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In this live demo, we'll show how Traction Complete uses agentic scoring to evaluate what's actually inside each record before any merge runs. Every record gets a 1 to 10 score with plain-English reasoning, plus a normalization agent that cleans data before matching to lift match rates and cut false positives.

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This demo is for you if:

• Your revenue team can't trust the CRM, and it starts with accounts

• Forecast accuracy depends on clean data you're not confident you have

• Your dedupe tool's "winning record" logic doesn't reflect real account value• Activity and pipeline are split across duplicates

• Manual review makes deduplication unsustainable at scale

• You're investing in AI, but the account data feeding it isn't clean