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 expanded our content moderation scoring to include product review signals, ensuring brand-safe data for customer-facing use cases.

Affected Signals

• G2 Reviews
• Glassdoor Reviews
• Reddit, LinkedIn, Twitter/X, YouTube (already supported)

How to Use

Filter by moderation_score (0.0–1.0, higher = safer):

{
  "signal_type": "g2-review",
  "data": {
    "moderation_score": 0.95,
    "review_text": "Great product for enterprise teams...",
    ...
  }
}

Recommended Thresholds

Manifest files are now generated for all signal data drops, enabling event-driven data pipelines.

After each data delivery, a manifest file is written to a dedicated manifest bucket. Each signal type gets its own manifest file per delivery date.

For full details on manifest location, schema, and usage, see our Manifest Files guide.

Quick Example

File: news_2026-04-07.json

{
  "signal_type": "news",
  "delivery_date": "2026-04-07",
  "status": "complete",
  "destination": "internal",
  "deliveries": [
    {
      "delivery_timestamp": "2026-04-07T00:00:00Z",
      "data_path": "gs://autobound-news-v3/2026-04-07-00-00-00/",
      "files": [
        {
          "file_name": "output.jsonl",
          "file_path": "gs://autobound-news-v3/2026-04-07-00-00-00/output.jsonl",
          "format": ".jsonl",
          "size_bytes": 79510730,
          "record_count": 16766
        },
        {
          "file_name": "output.parquet",
          "file_path": "gs://autobound-news-v3/2026-04-07-00-00-00/output.parquet",
          "format": ".parquet",
          "size_bytes": 39160557,
          "record_count": 16766
        }
      ],
      "record_count": 33532
    }
  ],
  "total_record_count": 33532,
  "total_file_count": 2,
  "pipeline_run_id": null,
  "created_at": "2026-04-07T14:36:33Z"
}

We've increased the refresh frequency for all SEC filing signals from quarterly to monthly, ensuring you get financial insights closer to when they're filed.

Affected Signals

Why It Matters

SEC filings contain high-value signals — leadership changes, acquisitions, risk factors, strategic initiatives — but they go stale fast. With monthly refresh:

• Catch CFO/CEO changes within weeks, not months
• Surface M&A activity before your competitors
• React to risk disclosures while they're still relevant

What's Next

We're targeting weekly refresh for all SEC filings by February 2026. See our Roadmap for details.

→ View 10-K Schema
→ View Earnings Transcript Schema

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#/