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Concept — background and overview.
Semantic Search uses AI to understand natural language queries, allowing you to search for companies based on business context, attributes, and characteristics — not just names or URLs. This enhancement to Affinity’s global search helps you discover companies using the same language you’d use when talking to a colleague. What it does: Transforms global search from a basic name lookup tool into an intelligent discovery engine that understands concepts like “healthcare companies my firm met with last week” or “companies backed by Sequoia Capital.” Current release: Company search only. People search coming April 2026. Who can access: Affinity customers enrolled in the Closed Beta via Labs How to enable: Settings > Labs > Turn on “Search with AI” Where to use: Global search bar (Cmd+Option+K or Alt+K) with “Search with AI” option

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Key Concepts and How It Works

Traditional keyword search (current default):
  • Matches exact words you type
  • For companies: Only searches name and website URL
  • Requires you to know exactly what you’re looking for
  • Example: “Stripe” finds Stripe, but “payment processing company” finds nothing
Semantic search (AI-powered):
  • Understands the meaning and context of your query
  • Searches across company attributes like industry, location, interactions, and relationships
  • Recognizes concepts, synonyms, and business context
  • Works with natural language descriptions
  • Example: “payment processing companies” finds Stripe, Square, and other relevant companies

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How Semantic Search Works

When you enter a natural language query:
  1. AI analyzes your query to understand what you’re asking for:
    • Attributes mentioned (industry, location, investors)
    • Context clues (“my firm met with”, “in our pipeline”, “in our network”)
    • Time references (“last week”, “recently”, “this quarter”)
    • Business concepts (“healthcare”, “AI companies”, “solar energy”)
  2. Searches across available company data including:
    • Affinity Data: Industry, description, location, investor information
    • Smart Attributes (firm-wide): First/Last Contact, First/Last Meeting, First/Last Email, Next Meeting
    • Relationship Intelligence (firm-wide): Interaction history (meetings, emails), relationship scores
    • CRM Context: Org-relevant status (companies in your firm’s CRM)
  3. Ranks results by relevance:
    • Prioritizes org-relevant companies (companies your firm knows) over universal dataset
    • Considers how well each company matches your query
    • Shows most relevant results first
  4. Returns results typically within 10 seconds with:
    • Matching companies
    • Explanation of how results were found
    • Ability to refine your query if results don’t match expectations
IMPORTANT — Firm-Wide Data Only: When you use “I” in queries (e.g., “companies I met with last week”), the AI interprets this as your FIRM’s interactions, not your personal interaction history. Semantic search uses firm-wide data only:
  • Interaction dates reflect ANY team member’s interactions
  • Relationship scores are firm-wide maximum
  • “I” and “we” in queries mean “my firm”
  • You cannot search for “companies only I personally contacted”

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What Data is Searchable

For Companies (January 2026): Affinity Data:
  • Name and domain
  • Description
  • Industry
  • Headquarters location
  • Investor information (investors who have funded the company)
Smart Attributes (Firm-Wide):
  • First/Last Contact with your firm
  • First/Last Meeting with your firm
  • First/Last Email with your firm
  • Next Meeting scheduled with your firm
Relationship Intelligence (Firm-Wide):
  • Interaction history (meetings and emails between company and your firm)
  • Relationship scores (firm-wide max relationship strength)
CRM Context:
  • Org-relevant status (companies your firm has interacted with or added to Affinity)
  • Note: “In our pipeline” searches org-relevant companies, NOT specific list membership

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Use Semantic Search When:

You know characteristics but not the name:
  • “Solar energy companies in our pipeline”
  • “Healthcare companies in Boston”
  • “Companies in the payments space”
You’re searching by firm interactions:
  • “Healthcare company my firm met with last week”
  • “Companies we haven’t contacted in 90 days”
  • “Companies our firm had meetings with this quarter”
You’re searching by investors:
  • “Companies backed by Sequoia Capital”
  • “Companies backed by both Andreessen Horowitz and Sequoia”
You’re searching by relationship strength:
  • “Early stage companies with low relationship scores”
  • “Companies where we have strong connections”
  • “Companies with no recent contact”
You’re exploring or discovering:
  • “AI companies in San Francisco”
  • “Solar energy companies that use battery storage technology”
  • “Healthcare companies focused on cancer diagnosis”
You don’t remember exact details:
  • “That biotech company someone at our firm talked to at the conference”
  • “The solar company mentioned in our partner meeting”

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Use Keyword Search When:

You know the exact name:
  • “Stripe” (instant results)
  • “OpenAI” (direct match)
You have domain:
  • “stripe.com”
  • “openai.com”
You want instant results:
  • Keyword search is faster (less than 1 second vs ~10 seconds)
  • Better for quick lookups when you know what you’re searching for

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Example Queries

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Supported Company Search Examples (January 2026)

By Industry and Location:
  • “Healthcare companies in our network”
  • “Fintech startups in San Francisco”
  • “Climate tech companies in Boston”
  • “Enterprise SaaS companies in New York”
  • “AI companies focused on healthcare”
By Investors:
  • “Companies backed by Sequoia Capital”
  • “Companies backed by both Bain Capital and Bessemer”
  • “Portfolio companies of Andreessen Horowitz”
By Firm Interactions:
  • “Solar energy company my firm met with last week”
  • “Companies we haven’t contacted in 90 days”
  • “Healthcare companies our firm had meetings with this quarter”
  • “Companies we emailed recently”
By Relationship Strength (Firm-Wide):
  • “Early stage companies with low relationship scores”
  • “Companies where we have strong connections”
  • “Companies with weak relationships”
By Industry Concepts and Technology:
  • “Payment processing companies”
  • “AI companies focused on enterprise automation”
  • “Solar energy companies using battery storage technology”
  • “Healthcare companies focused on cancer diagnosis using AI”
  • “Biotech companies working on extracellular vesicles”

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How to Access and Use

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Enabling Search with AI

  1. Navigate to Settings > Labs
  2. Find “Search with AI” feature
  3. Toggle ON
  4. Return to global search

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Using Search with AI

  1. Open global search (Cmd+Option+K or Alt+K)
  2. Click “Search with AI” button
  3. Type your natural language query
  4. Press Enter
  5. Review results (appears in ~10 seconds)
  6. Click any company to view profile
  7. Edit query and re-run if needed
Tips:
  • Example queries are shown in the search interface
  • Click an example to pre-fill your search bar
  • Query stays visible for easy editing and refinement

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Best Practices

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Writing Effective Queries

Be specific:
  • “Companies” (too broad)
  • “Healthcare companies in Boston” (specific attributes)
Use natural language:
  • “Show me AI companies our firm met with recently”
  • “Find payment processing companies”
  • “Which healthcare companies are in our network?”
Combine multiple criteria:
  • “Healthcare companies in Boston our firm met with this quarter”
  • “AI companies backed by Sequoia that we haven’t contacted”
Use time references naturally:
  • “Last week”, “recently”, “in the past 6 months”
  • “This quarter”, “this year”
  • “Haven’t contacted in 90 days”
Leverage relationship context (firm-wide):
  • “In our network” (org-relevant companies)
  • “In our pipeline” (org-relevant companies)
  • “Our firm met with”, “We haven’t contacted”
Add industry detail for better results:
  • Instead of just “healthcare”, try “healthcare companies focused on cancer diagnosis”
  • Instead of “AI”, try “AI companies building enterprise automation tools”
  • Instead of “energy”, try “solar energy companies using battery storage”
Remember “I” = “my firm”:
  • “Companies I met with” searches your FIRM’s meetings, not just yours personally
  • “I haven’t contacted” searches your FIRM’s contact history

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Technical Notes

Performance:
  • Results typically return within 10 seconds
  • May take longer for very complex queries
  • Keyword search remains faster (less than 1 second) for simple name lookups
Data freshness:
  • Newly created companies searchable within ~10 seconds
  • Updates to company data available in search within ~10 seconds
  • Interaction data (meetings, emails) syncs continuously
Privacy and permissions:
  • Semantic search respects same permissions as keyword search
  • You only see companies you have permission to view
  • Interaction history is firm-wide (not individual-specific)
  • Relationship scores are firm-wide max relationship strength
Result limits:
  • Returns most relevant results (typically 10-20 companies)
  • Can refine query for different results
  • For comprehensive analysis, use Lists filters instead

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Frequently Asked Questions

How is this different from regular search? Regular keyword search only matches exact company names and URLs. Semantic search understands what you’re describing and searches across industry, location, investors, and firm interaction data. Do I need to use special syntax? No. Write queries like you’re asking a colleague: “Show me healthcare companies in Boston” or “Find companies backed by Sequoia.” The AI understands natural language. Does this replace keyword search? No. Keyword search remains the default and is still best for quick name lookups. Semantic search is an optional enhancement for contextual discovery. How long do results take? Semantic search typically returns results within 10 seconds (vs less than 1 second for keyword search). The AI needs time to understand your query and search across data. Does semantic search use my personal interaction history or firm-wide data? Semantic search uses FIRM-WIDE interaction data only. When you search for “companies I met with last week,” the AI searches for companies YOUR FIRM met with, not just your personal meetings. How do I know which mode I’m using? You’ll see “Search with AI” in the global search interface. You must enable it in Settings > Labs first. Keyword search remains the default. How are search results ordered, and how can I prioritize companies my firm knows? Semantic search ranks results using multiple factors:
  1. Semantic relevance score (primary): How well the company matches your query across all attributes
  2. Org-relevance (secondary): Companies your firm knows are prioritized but not guaranteed first
  3. Data completeness (tertiary): Companies with richer descriptions and data rank higher
To limit results to only companies your firm knows, add these phrases to your query:
  • “in my network”
  • “in our pipeline”
  • “companies we know”
What does “in our pipeline” actually search? “In our pipeline” searches org-relevant companies (companies your firm has interacted with or added to Affinity). It does NOT search for companies in a specific list named “Pipeline.” To search within a specific list, use Lists filters or Find in View instead. Do search results show data for companies not in my network? Yes. As of January 2026, out-of-network company profiles show global data (Affinity Data and Dealroom based on your subscription). This allows you to evaluate company fit during sourcing without adding them to your CRM first. Note: Crunchbase and PitchBook data remain exclusive to org-relevant companies (companies in your network) due to licensing agreements. Is this available to everyone? Currently available as a Labs feature (Closed Beta) to select customers starting January 2026. Must be enabled in Settings > Labs. Broader availability to be announced. How accurate are the results? Accuracy depends on data quality in your CRM. The AI uses available Affinity Data and Smart Attributes to find matches. Companies with more complete descriptions and accurate industry tags are more discoverable.