Autobound's generate-insights API uses a robust resolution waterfall to identify contact and company information, ensuring high-quality insights even with minimal input data.

This allows you to generate meaningful insights using only a contact's email, LinkedIn URL, or even just the company's domain.

Even if you pass in [email protected] as the contact, our engine would generate insights for Cisco.

Resolving different data points enables our engine to surface different insights. Examples:

• Resolving the contact's company enables us to surface 10ks, case studies, buying committees, etc.
• Resolving the contact enables us to resolve the company, LinkedIn posts, etc.

Here's how our resolution waterfall works, and how you can improve content quality over time.

How Resolution Works

The resolution process follows these steps:

1. Contact-Level Resolution:

   • Email Address: If a contact email is provided, the system attempts to:
     • Identify the contact’s LinkedIn profile.
     • Resolve the contact’s company and associated insights (e.g., news, 10-K filings).
     • Generate insights like LinkedIn posts, podcasts, or job changes.
   • LinkedIn URL: If a LinkedIn URL is provided, the system directly resolves:
     • LinkedIn posts.
     • Social media appearances.
     • Associated company information.

2. Company-Level Resolution:

   • Company Domain: If only a company domain is provided, the system resolves:
     • Company-level insights, such as 10-K filings, hiring trends, news articles, or financial updates.

3. Fallback Scenarios:

   • If the contact’s email is generic (e.g., [email protected]), resolution may be limited to basic contact information. While the system often resolves the company or LinkedIn URL even in these cases, it’s recommended to provide as many identifiers as possible for higher accuracy.

Recommended Best Practices

To maximize the quality and relevance of insights:

• Provide Multiple Identifiers: If possible, pass in contactEmail, contactLinkedinUrl, and contactCompanyUrl.

• Example: With just contactEmail, the system can:

  • Resolve the contact’s LinkedIn profile and fetch LinkedIn posts.
  • Identify the contact’s company to retrieve company-level insights like 10-K filings and financial trends.

• If only a generic email (e.g., [email protected]) is provided, resolution is more challenging. Providing the company domain (e.g., autobound.ai) or LinkedIn URL increases success rates.

Examples of Insights by Resolution Method

Recommendations for Success

• Use All Identifiers: Maximize resolution success by providing both contact and company identifiers.
• Understand Fallbacks: When using generic emails, include additional identifiers (e.g., company domain) to improve resolution.
• Ensure Data Accuracy: Providing accurate identifiers improves the relevance and quality of retrieved insights.

By understanding the resolution process and providing complete identifiers, users can ensure they get the most out of Autobound’s generate-insights API.

Implementation

Start with just an email, LinkedIn URL, or company domain:

{
    // Contact (Prospect) Identifier - only one needed
    "contactEmail": "[email protected]",
    // OR
    "contactLinkedinUrl": "linkedin.com/in/henry-schuck",
    // OR
    "contactCompanyUrl": "zoominfo.com"
}

With this minimal input, the API resolves:

• Contact information (name, title, location, etc.)
• Company details (domain, industry, size, etc.)
• Contact insights
• Company insights

Fill Rates

• Email/LinkedIn URL: 95% match rate
• Domain Only: 99% match rate for company data