ICP scoring that updates the moment enrichment fires
Set signal weights — Funding Recency, Headcount Growth, Tech Stack Match, Intent Tier — and define your fit-tier thresholds once in the Salmon dashboard. Every enrichment that fires runs your model against the fresh data and writes a Strong / Moderate / Weak fit tier back to Salesforce before the lead enters any rep's queue.
Your ICP model, applied in real time
Configure signal weights to match your buyer profile. Salmon applies the model automatically — no re-training required when enrichment signals update.
Four configurable signal weights
Three output fit tiers
Configurable by a RevOps manager, not a data science team
Salmon is not a machine learning platform. There is no model training cycle, no feature engineering, no data science dependency. You set four signal weights via a dashboard slider and the model runs on the next enrichment. Change a weight — it takes effect immediately, no redeployment required.
Score triggers routing automatically
A Strong ICP score writes sln_fit_tier__c = "Strong" to the Salesforce Lead record — and your existing Lead Assignment Rules read that field value to route automatically. No custom Apex, no new routing logic to write.
Salmon doesn't replace your routing infrastructure — it feeds it a signal it's never had before. The enrichment payload includes fit_tier, fit_score, and latency_ms. Your rules read the tier; the score is available for custom logic if you need it.
Configure your ICP model in minutes.
Connect Salesforce or HubSpot, set signal weights, and your first enrichment runs in under an hour.