Scores leads before your rep calls, predicts churn 30 days early, recommends lanes to cross-sell.
Every action logged. Every decision explainable.
Ingests from web, email, RFQs. Enriches with volume, industry, lanes.
ML scores conversion: volume, lanes, industry, engagement, timing.
30+ signals — volume decline, complaints, payment delays, competitor RFQs.
Matches patterns with capacity/margin. Recommends lanes.
Volume, lane compatibility, industry, engagement.
30+ signals. Flags 30 days before cancel.
Specific upsell lanes with projected revenue.
Lifecycle follow-ups: welcome, reviews, win-back, upsell.
Revenue per customer, profitability, wallet share.
Carrier on-time %, claims, communication. Updated weekly.
92-score gets top rep. 34-score auto-nurtures. 3.2x conversion.
$1.2M account flagged early. Proactive outreach retained it.
400+ scorecards. Auto-recommends preferred per lane.
Answers from our implementation team.
Traditional CRMs store activities; our agent scores propensity to buy, churn risk, and upsell lanes, then triggers the right play—task, cadence, or alert—in Salesforce, HubSpot, Zoho, or our native views.
We blend firmographics, lane history, tender and invoice patterns from TMS, engagement signals from email and web, and win/loss notes so scores reflect how freight businesses actually behave.
In reference deployments roughly three quarters of accounts that ultimately churned were flagged at least thirty days ahead, with a low double-digit false-positive rate that improves as we tune thresholds for your book.
Yes. Integrations are bidirectional: we read opportunities and contacts, write tasks, stage changes, and recommended lanes back into the CRM objects your reps already live in.
We refresh TMS-derived service metrics—on-time pickup, tender acceptance, claims ratio, invoice timeliness—on a weekly cadence (or faster when APIs allow) so lane recommendations align with reality on the street.
See AI CRM running on your actual freight data.
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