Does Crunchbase actually train AI models, or does it just get retrieved live?

Both happen. Crunchbase's public data has been part of the broader web corpus that large language models train on for years, but the July 2026 launch of Crunchbase's MCP server added a live retrieval channel, letting Claude, ChatGPT, and Gemini query its 790-field company records directly at answer time rather than relying only on what was in training data.

How often should I update my Crunchbase profile?

At minimum after every funding event, leadership change, or category shift, and as a floor, review the full profile at least twice a year. Crunchbase's own ranking algorithm treats different fields as decaying at different rates, with funding and news data going stale fastest, so a profile last touched a year ago is already working against you.

What is the fastest fix if I only have an hour?

Correct your business name, address, and phone number at the three major data aggregators, Data Axle, Foursquare, and Neustar Localeze, since those corrections propagate to the widest network of downstream directories with a single edit each.

Do small or early-stage companies need a Crunchbase profile at all?

Yes, if you want to be recognized as an entity in AI-generated answers about your category or competitive set. An unclaimed or nonexistent profile means there is no structured record for a model to retrieve, so it either omits you or falls back on whatever thin, secondhand mentions exist elsewhere.

Should I prioritize Crunchbase or G2 and Capterra first?

It depends on your business. B2B software companies should treat G2 and Capterra as equally urgent, since G2 alone is the fourth most-cited source on ChatGPT, while Crunchbase matters more heavily for funding, category, and company-profile questions across every industry, not just software.

Does fixing directory data help with Google's AI Overviews too?

Indirectly, yes. Consistent, complete directory and Crunchbase data strengthens your underlying knowledge graph entity, which both classic Google systems and AI Overviews draw on, though AI Overviews weigh a broader mix of ranking and content signals beyond directory data alone.

What is entity resolution and why does it matter here?

Entity resolution is the process a search or AI system uses to decide that multiple records across different sources refer to the same real-world company. Consistent name, address, and category data lets that resolution happen cleanly and merges every source into one confident, well-supported entity, while inconsistent data risks fragmenting your company into two weaker, partial records.

How do I know if an AI model is citing outdated directory data about my company?

Run the same set of direct prompts, "what is [company]," "how many employees does [company] have," "who funds [company]," across ChatGPT, Claude, Gemini, and Perplexity on a recurring schedule and compare the answers to your corrected source-of-truth document. Doing this manually is slow and easy to let slip, which is exactly the gap a daily AI-visibility tracking tool like Promptive is built to close.

How to Optimize Your Crunchbase and Business-Directory Profiles for AI