The Identity Problem
Your business data is scattered across tools, and each tool has its own way of identifying people, companies, and projects:
- Slack: @john.doe
- CRM: John Doe, Acme Corp, Deal #4521
- Email: john@acmecorp.com, JD, “John”
- Project tool: J. Doe assigned to Task #891
A human reading these instantly knows they’re all the same person. A system that doesn’t perform entity resolution sees four unrelated entries.
Why It Matters
Without entity resolution, your knowledge graph is fragmented. Information about the same client exists in multiple disconnected nodes instead of one rich, comprehensive profile.
With entity resolution: One node for “John Doe at Acme Corp” that connects to all his emails, Slack messages, CRM records, project tasks, meeting notes, and preferences — regardless of how each tool labels him.
Without entity resolution: Five separate fragments of information that never connect. The system can’t tell you that the “JD” who mentioned budget concerns in Slack is the same “John Doe” whose contract renewal is next month.
How It Works
Entity resolution uses multiple signals to match references:
- Name similarity — Fuzzy matching handles abbreviations, typos, and variations
- Email matching — Shared email addresses are strong identity signals
- Contextual clues — If “JD” and “John Doe” appear in the same project contexts, they’re likely the same person
- Cross-reference — CRM records link email addresses to full names, which link to Slack handles
- Temporal patterns — Communication patterns and activity timing help disambiguate similar names
The result is a unified identity graph where every mention, across every tool, resolves to the correct real-world entity.