We've made the following updates to the Earnings Transcript signal schema to improve signal quality and provide richer context for sales outreach.

New Fields Added: Speaker Attribution

We've added speaker attribution to help you reference who said what in your outreach. Instead of just having quotes, you now know if it was the CEO, CFO, or another executive—making your outreach more credible and personalized.

Example usage:

"In your Q3 earnings call, your CEO Michael O'Sullivan mentioned you're 'aggressively going after' the performance gap with competitors. We help retailers close that gap—worth a quick chat?"

Format Changes

Migration Notes

• evidence_speakers: This is an additive, non-breaking change. The new field provides speaker attribution for quotes in the evidence array. The evidence field itself remains unchanged as array[string]. If you don't need speaker info, you can ignore this field.

• earnings_date: This is a breaking change if you're parsing dates with strict ISO 8601 parsers. Update your date parsing logic to handle the new YYYY-MM-DD HH:MM:SS format (no T separator, no timezone suffix).

Affected Data

• All historical earnings transcript data (2025-01 through 2026-01) has been regenerated with the new schema
• Data delivered prior to January 11, 2026 uses the old schema (no evidence_speakers, ISO 8601 dates)
• Action required: If you received earnings transcript data before January 11, 2026, contact your account manager to request a re-sync to get the updated schema

Questions?

Contact [email protected] if you have questions about this update or need assistance with migration.

We’ve added support for Commented on LinkedIn Post as both an insight and a signal.

• As an insight: Captures when a prospect comments on a LinkedIn post, including the full comment text, sentiment, engagement metrics, and tags. It also includes details about the parent post (content, author, tags, and engagement), giving sellers both the prospect’s perspective and the broader context of the discussion.

• As a signal: Can now be used to automatically trigger campaign enrollment when prospects engage meaningfully in LinkedIn conversations. This allows GTM teams to take action the moment a prospect expresses opinions, challenges, or interests tied to their value proposition.

By combining both sides of the interaction—the comment and the post itself—you can pinpoint not only what your prospect cares about but also the broader conversation they’re joining.

Learn more in the full docs: Commented on LinkedIn Post API

Changelog Entry

We've significantly upgraded our Job Change insight. Previously, Job Change insights were historically available only within generated content. Now, they're fully supported as dedicated Signals, enabling proactive notifications when prospects join new companies, get promoted internally, or start their own ventures.

Additionally, we've enriched the insight's metadata to include detailed context on both the prospect’s previous and new roles, such as company URLs, employment dates, job locations, and role descriptions—giving your content even deeper personalization potential.

Use it to:

• Quickly respond when a champion leaves a key customer account.
• Adapt immediately if a critical stakeholder on an active opportunity switches roles.
• Proactively target newly hired decision-makers in your ideal customer profile.

Learn more in our Job Change documentation →

We’ve added support for a new insight subtype: glassdoorCompanyReviewsAndRatings. This insight aggregates real employee feedback from Glassdoor to uncover internal sentiment on culture, leadership, compensation, DEI, and more.

Use it to:
• Reference authentic pain points in outbound messages • Highlight cultural or operational gaps when positioning your solution • Build social proof by citing feedback from employees directly

This insight is available in the Generate Content API, the Generate Insights API, and can also be used as a trigger condition within Signal Engine—for example, to surface companies with low leadership or culture ratings.

📘 View API Docs

This release marks a major upgrade to how Autobound determines which insights matter most when generating outbound messaging.

Read the full blog post, here.

🔍 What’s New

User-Aware Insight Relevance

You can now pass optional user fields—like userEmail, userCompanyUrl, or userLinkedinUrl—to personalize how insights are ranked.

This is especially powerful for:

• OEM platforms building on Autobound (e.g. TechTarget, AI SDR)
• Teams running campaigns on behalf of other sellers or orgs
• Anyone needing insights tailored to a different seller’s POV

AI-Driven Ranking (LLM Upgrade)

We’ve replaced our legacy v1.2/v1.3 scoring system with a real-time LLM that evaluates ~100 data points across the prospect, user, and both companies.

🧠 From Unique → Relevant: A New Era for Insight Quality

Autobound’s first 5–7 iterations of the generate-insights API focused on surfacing unique insights—sourced from 10-K filings, podcasts, LinkedIn posts, earnings calls, news, and more. The priority was breadth and richness: help sellers say something their competitors weren’t.

Now, for many prospects, Autobound can generate 100+ insights across dozens of data categories. But not all of those insights are equally useful, especially in fast-moving sales workflows. And for our API partners—who might be generating insights on behalf of thousands of sellers across many orgs—ensuring the right insight lands in front of the right user became critical.

🎯 What’s Changed with v1.4

With v1.4, we’ve shifted from simply what’s available to what’s most relevant. That means:

• Ranking matters more than resolution.
  Insight richness is no longer the bottleneck—relevance is.

• You don’t need to pre-filter insights manually.
  The new engine auto-ranks every result based on what the user reaching out is likely to care about. No need to toggle off irrelevant insights or build custom logic on top of our response.

• The API is finally user-aware.
  By passing userEmail or userCompanyUrl, insights are filtered and prioritized for that individual’s ICP, industry, and product focus—without additional logic on your side.

🚀 Bottom Line

If you’re embedding Autobound into your product, running a sales assistant workflow, or just want to help your reps send sharper messaging—v1.4 removes the guesswork.

You’ll still get rich signals across news, growth, hiring, and behavior—but now with AI-driven precision that adapts to who's doing the outreach.

📚 Learn More

• Main API Reference
• Contact & Company Resolution Logic
• User-Level Parameters

You can now generate insights tied to recent Product Hunt launches, including product name, tagline, launch date, category tags, vote count, and more. This makes it easy to identify when a company is actively going to market, launching a new initiative, or positioning in a competitive category—perfect context for timely, relevant outreach.

This is especially valuable for GTM, Product, and Growth personas, where launches often signal budget, urgency, or a shift in strategy.

Coming Soon: With the upcoming Signal Engine release, you’ll be able to automatically track Product Hunt launches as signals and have companies suggested to you the moment they go live—no manual research needed.

→ View full docs

We’ve added full support for surfacing a prospect’s recent Twitter/X activity.

Just pass contactEmail or contactLinkedinUrl in your request—Autobound will resolve the correct handle and return profile metadata, recent tweets, and engagement stats. Use enabledInsights / disabledInsights to control whether tweets are referenced in generated content.

Read the docs → https://autobound-api.readme.io/update/docs/twitter-post#/

What’s new?

A full list of all current insight subtypes can be found here .

Why it matters

• Instantly surface how much each company is investing in core support functions.
• Auto-generates summaries + contextual trend language (“expanding rapidly,” “trimming moderately,” etc.).
• Returns code 200 when headcount > 0, 400 when none—ideal for quick filtering.
• Perfect for tailoring outreach around growth, efficiency, and scale-enablement value props.

Docs

• Finance: https://autobound-api.readme.io/update/docs/finance-employee-breakdown-and-growth#/
• HR: https://autobound-api.readme.io/update/docs/hr-employee-breakdown#/

Start calling these subtypes today to add richer operational context to your prospect signals!

We’ve launched two powerful new POST endpoints that allow you to query rich B2B contact and company data using highly flexible filters. This update enables users to programmatically retrieve structured data on millions of companies and decision-makers with pinpoint control.

Example Use cases:

1. Find sales leaders at Microsoft in California. Use filters like domain, department, and location to surface precise outreach targets—no manual research required.
2. Find every Chief Revenue Officer at $200M+ companies. Combine title and revenue filters to instantly identify senior buyers—ideal for pipeline building or TAM analysis.
3. Product Managers can build a contact suggestion feature. Show users the right people to reach out to when they land on a target account page—powered by dynamic queries to our B2B contact API.
4. RevOps teams can enrich contacts in a territory automatically. Run scheduled queries by geo, title, or company size to keep CRM records fresh and actionable—no CSVs required.

🧑‍💼 POST /filter-contacts

Search B2B contact records using attributes like:

• Demographic filters: FirstName, LastName, PersonalCity, PersonalState, PersonalZip, Gender, AgeRange, etc.
• Professional filters: JobTitle, Department, SeniorityLevel, LinkedInUrl
• Company context filters: CompanyDomain, CompanyName, CompanyNaics (partial support), and more

Each response includes rich fields like email, phone, LinkedIn, location, and job-level metadata. Supports pagination and sorting by fields such as FirstName, LastName, and UpId.

📄 View docs: Get Contacts

🏢 POST /filter-b2b

Retrieve B2B company records filtered by:

• Firmographics: CompanyName, Domain, Revenue, EmployeeCount
• Location: State, City, Zip, Country
• Industry & NAICS: PrimaryIndustry, CompanyNaics

These filters enable flexible firmographic targeting, great for GTM use cases like ICP building, TAM mapping, or competitive research.

📄 View docs: Get B2B Companies

✅ Highlights:

• Flexible Filters array for combining multiple field conditions
• Supports equals, not_equals, contains, and not_contains operators
• Pagination via Page and PageSize query params

We’ve introduced a new insight subtype that highlights where your prospects have lived or worked and where their company is headquartered. By revealing these geographic details, you can tailor your outreach to resonate with local references, market concerns, or regional events.

Key Features

Current Location: Understand exactly where a prospect is based today, so you can reference local events, communities, or news.
Past Locations: Quickly see prior places they’ve worked or lived, which might offer shared experiences or networking ties.
Company HQ: Recognize which region the company calls home, providing context for corporate culture, local market conditions, and potential scheduling considerations.
Contextual Relevance: Leverage location data to build stronger rapport, especially if you share a common location or have local customer references.
Customizable Control: Enable or disable location insights in your requests so you only get data when needed.

Example Usage

Generate Content API

{
  "enabledInsights": ["workExperienceLocationProfile"],
  "disabledInsights": []
}

When workExperienceLocationProfile is in enabledInsights, your API responses may include personalized mention of the prospect’s geographic history.

Generate Insights API

{
  "insightSubtype": "workExperienceLocationProfile"
}

Specify "insightSubtype": "workExperienceLocationProfile" to retrieve the new location profile insight directly.

Example Payload

Below is a sample data block illustrating the information we gather under the workExperienceLocationProfile subtype:

{
  "locationProfile": {
    "currentLocation": "Orange County, California, United States",
    "previousLocations": [
      "Greater Chicago Area",
      "Tel Aviv, Israel",
      "Herzliyya, Tel Aviv, Israel",
      "Ramat Aviv, Israel"
    ],
    "companyLocation": "Chicago, US"
  }
}

Example Output

"I noticed you’re currently in Orange County – we’re hosting a local event on creating pipeline with genAI. Thought you might be interested!"

By referencing a prospect’s move from Chicago to Orange County, for instance, you can spark meaningful dialogue that acknowledges their new region and establishes deeper rapport.

Unlock Location-Driven Personalization

Connect your data sources or CRM to automatically surface location context for every prospect. Whether you’re inviting them to an event, referencing a shared city, or acknowledging prior workplaces, the Work Experience Location Profile insight streamlines your ability to speak your prospect’s language in every outreach.