Claude Just Got Access to Your Buyer's Research Stack. Here's Your 4-Step Data Audit.

On February 24, Anthropic connected Claude directly to FactSet, LSEG, DocuSign, MSCI, and S&P Global.

That's the due diligence stack. The tools enterprise analysts and procurement teams use to vet vendors before anyone reaches out to sales. And Claude can now query all of it in a single conversation.

This isn't hypothetical future behavior. It's a deployed feature with enterprise controls, private plugin marketplaces, and PwC already signed on for CFO-office use cases. The 9-10 month adoption curve means you have a window — but it's not permanent, and it's not wide.

Here's the four-layer audit to run this week.

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Layer 1: FactSet / LSEG Presence (Market Research Layer)

When a financial analyst or CFO asks Claude to research your category using FactSet or LSEG, it's querying market data that includes:

• Press coverage and media mentions in financial publications
• Analyst research and equity commentary (if public)
• Company fundamentals data (for public companies) or comparable benchmarking (for private)
• Industry position based on coverage density

What to audit:

• Search your company in Crunchbase and Pitchbook — these feed FactSet's private company data layer
• Check whether your company profile is current: founding date, funding rounds, employee count, product category
• Check whether recent press coverage is linked — FactSet pulls from the same publication set as major financial media
• Search Google Finance and Bloomberg for any references to your company name — signals whether you appear in financial context at all

What to fix:

• Update Crunchbase and Pitchbook immediately if data is stale or absent
• Prioritize earned media placement in business publications that FactSet's data layer weights (WSJ, Forbes, Business Insider, TechCrunch — all indexed in financial data feeds)
• Get at least one press piece that places you in a category context — "company X is the leading [category] for [ICP]" — since this is the signal Claude uses to determine competitive positioning

The gap most companies have: their Crunchbase profile was last updated during their last funding round. FactSet and LSEG treat recency and coverage density as credibility signals. A dormant or thin profile reads as an inactive or marginal player.

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Layer 2: Press Footprint and Citation Density

This is the layer that Claude weights most heavily when generating vendor analysis. Not your own content. Not your website. Third-party press mentions in sources the AI engine treats as authoritative.

Research on LLM citation behavior shows that AI agents consistently reference the same authoritative publication set: major business media, trade verticals, and high-authority publications that appear in both human and AI search results.

What to audit:

• Search [your company name] site:techcrunch.com OR site:forbes.com OR site:businessinsider.com OR site:inc.com — count the actual hits
• Run the same search for your two closest competitors — this is your gap map
• Check whether recent press coverage includes your category keywords — not just your company name, but "X is the [category] for [market]"
• Use Google's About This Result to see what AI Overviews are saying about you (available in Chrome)

What to fix:

• If competitors have 3x your press volume, that gap is reflected in Claude's outputs today
• The fastest path is earned media placement — not content production. Placing in a Forbes or TechCrunch coverage piece generates the citation signal that AI engines reference, not a new blog post
• Priority publication tiers for AI citation weight: Tier 1 (Forbes, TechCrunch, WSJ, BI), Tier 2 (industry verticals, trade publications), Tier 3 (regional business journals)

A useful benchmark: Profound's data shows 97% of enterprises see measurable improvement in AI citation frequency within 3-6 months of an earned media investment. The companies shipping this week are setting up their position for Q3.

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Layer 3: DocuSign Signal (Customer and Deal Velocity Proxy)

This one is less obvious, but it's worth understanding. DocuSign's enterprise data is used as a market signal indicator — contract volume and deal velocity across industries create a de facto activity map. While Claude won't pull your company's specific contracts, the plugin enables Claude to reason about deal activity patterns in a category.

More importantly, DocuSign integration means Claude can assist buyers in reviewing vendor contract terms, SOWs, and master service agreements. What you want to control here is what Claude sees when a buyer pastes in your standard contract or asks it to compare your SLA terms against competitors.

What to audit:

• Review your standard contract language — is your SLA specific and credible, or does it have ambiguous carveouts?
• Check whether your pricing page and contract terms create friction signals (e.g., long lock-in periods, unusual cancellation clauses) that AI analysis might flag as risk

What to fix:

• If your MSA has 3 pages of legalese for a mid-market SaaS contract, simplify before this pattern gets analyzed at scale
• Ensure your proposal template leads with specific outcome commitments — these read as strong credibility signals in AI-assisted contract reviews

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Layer 4: Entity Consistency (The Baseline)

All of the above fails if your entity data is inconsistent. Claude and other AI systems build a model of your company from multiple sources. If your company name, category label, founding year, and core value proposition are stated differently across LinkedIn, Crunchbase, your website, and press coverage — the AI constructs a fragmented, lower-confidence entity profile.

What to audit:

• Check LinkedIn, Crunchbase, Google Business Profile, and your website "About" page — is the company description consistent?
• Run [your company name] in ChatGPT and Perplexity and read the response carefully — inconsistencies in AI outputs reveal which data sources are fighting for authority
• Check your Wikipedia page if one exists — Wikipedia is heavily weighted in AI entity models

What to fix:

• Write one canonical 75-word company description that includes: company name, category, ICP, and primary value proposition — deploy it identically across all platforms
• If you don't have a Wikipedia page and your company is 3+ years old with verifiable press coverage, you likely qualify — a properly sourced Wikipedia entry is one of the highest-leverage entity authority moves available

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The Priority Order

Run these four layers in sequence:

1. Entity consistency (one afternoon, highest leverage per hour)
2. Press footprint audit (benchmark yourself against two competitors; identify the gap)
3. FactSet/LSEG data layer (update Crunchbase, Pitchbook, and prioritize financial publication coverage)
4. DocuSign/contract signal (clean up contract language before AI-assisted review becomes standard)

This is Machine Relations applied to enterprise: building the authority infrastructure that ensures you're in the room when your buyer's AI runs the due diligence check. Claude now has access to their research stack. The question is what it finds.

If you want to see exactly where your entity stands in the AI discovery layer right now, the visibility audit maps it out.

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The window: Anthropic's own data suggests 9-10 months for enterprise-wide Claude plugin adoption. That's your runway. The companies auditing and fixing their data layer in Q1 2026 will be the default citation in the enterprise AI research layer by Q4. The ones who wait will be playing catch-up against companies already embedded in the AI shortlist.

Run the audit. Fix the gaps. This week, not next quarter.