everyrow.io/screen
Filter with web research on every row
- Screen candidates by qualitative criteria
Case study: Job posts by remote work policy - Screen companies by investment theses
Case study: S&P 500 by geopolitical risk - Screen leads or customers by your criteria
Case study: Leads by their strategic needs - Run it yourself in Python
Notebook: LLM screening at scale
everyrow.io/rank
Score and rank with web research on every row
- Rank leads by signals that require research
Case study: Prioritize prospects even when your CRM data is fragmented - Score by ICP fit using qualitative reasoning
Case study: Investment funds by product fit - Research scattered data across many sources
Case study: Texas cities by permit processing time - Run it yourself in Python
Notebook: Score leads from fragmented data
everyrow.io/merge
Join tables using research to match rows
- Match products to vendors
Case study: Link software to vendors - Merge candidates coming from multiple sources
Case study: Unifying Hubspot records - Clean sales or company data without IDs
Case study: Join disparate data before CRM upload - Run it yourself in Python
Notebook: LLM merging at scale
everyrow.io/dedupe
Deduplicate with research-backed matching
- Clean company names that don't match
Case study: Dedupe company leads from your CRM - Resolve contacts with conflicting metadata
Case study: Clean candidate list across career changes - Deduplicate large lists of products
Case study: Remove duplicates from 20k FDA drugs - Run it yourself in Python
Notebook: Dedupe CRM company records
