What is the problem with current AI systems?

Most AI systems rely on incomplete data sources such as:

  • CRM exports

  • scraped websites

  • spreadsheet-based enrichment tools

  • traditional contact databases

These sources contain records but not relationships.

As a result, AI systems often generate answers that are confident but incorrect.

Even well-designed AI agents hallucinate when the underlying data lacks context.

Why does relational data matter?

AI agents must answer questions such as:

  • What subsidiaries belong to a company?

  • What suppliers support a manufacturer?

  • What companies compete in a specific capability area?

These questions require relationship data, not static records.

Traditional enrichment platforms store data in tables.Knowledge graphs store connections between entities.

How do AI agents use GraphIQ?

AI agents interact with GraphIQ through the GraphIQ MCP server.

This allows agents to query the graph dynamically.

Example agent workflows include:

  • identifying companies similar to a target account

  • mapping corporate hierarchies

  • retrieving relevant stakeholders across subsidiaries

  • incorporating real-time news signals into prospecting decisions

What queries can AI agents answer with GraphIQ?

Here are some examples?

  • What subsidiaries belong to a specific company?

  • Which companies supply a manufacturer?

  • What companies are owned by a private equity firm?

  • What companies are similar to this organization based on capabilities and technologies?

GraphIQ for Revenue Operations and GTM Engineers