Social Intelligence
Company Podcast Mention Signals
Executives say things on podcasts they would never put in a press release: unfiltered opinions, competitor takes, tech stack decisions, and pain points disclosed on air. Autobound turns those conversations into structured, company-level signals.
Companies Covered
Signal Subtypes
Podcast Publishers
Refresh Cadence
What Are Company Podcast Mention Signals?
Company podcast mention signals fire when a target account is discussed on a podcast: an executive shares an opinion, a product launch gets covered, a competitor is named, or a pain point is disclosed on air. Autobound indexes episodes from 20+ podcast publishers and extracts structured, company-level intelligence from what gets said.
Coverage is deliberately high-signal rather than high-volume: roughly 5,000 signals across 1,900+ companies since March 2026, refreshed monthly and classified into subtypes like executiveOpinion, competitorNamed, techAdoption, and painPointDisclosed. Each signal captures the episode title, show name, publication date, and an AI summary of what was discussed, giving reps 30-60 minutes of unscripted executive commentary to reference in outreach.
Example Signal Subtypes
Data Schema
Company Podcast Signal Schema
Each podcast signal includes episode metadata, an AI-generated summary, and extracted intelligence like pain points, technologies, and competitors mentioned, resolved to the company being discussed.
{
"signal_id": "a7b8c9d0-e1f2-4356-abcd-789012345678",
"batch_id": "2026-06-01-00-00-00",
"signal_type": "podcast-company",
"signal_subtype": "executiveOpinion",
"signal_name": "Executive opinion shared on podcast",
"association": "company",
"detected_at": "2026-06-13T09: 14: 37Z",
"company": {
"name": "Vercel",
"domain": "vercel.com", // match on domain
"linkedin_url": "linkedin.com/company/vercel", // or match on LinkedIn URL
"industries": ["Developer Tools", "Cloud Computing"],
"employee_count_low": 201,
"employee_count_high": 500,
"description": "Frontend cloud platform for building and deploying web applications..."
},
"data": {
"title": "Why the Edge Is Eating the Cloud",
"show_name": "Software Engineering Daily",
"website": "https://podcasts.apple.com/us/podcast/...",
"published_at": "2026-06-05T10: 00: 00Z",
"duration_minutes": 47,
"snippet": "Vercel's CTO on why AI-generated frontends will change how teams ship software...",
"summary": "CTO argues most engineering teams overpay for idle compute and predicts AI agents will write the majority of frontend code by 2027.",
"tags": ["AI", "Edge Computing", "Developer Tools"],
"pain_points": [
{"topic": "Teams overpaying for idle cloud compute", "intensity": 0.8}
],
"technologies_mentioned": [
{"name": "Kubernetes", "status": "migrating_from"}
],
"competitors_mentioned": [
{"name": "Netlify"}
]
}
}- GCS Bucket
- gs://autobound-podcast-company/
- Formats
- JSONL · Parquet
- Refresh
- Monthly
Applications
What teams build with Company Podcast Mention Signals
05 documented applications
- 01
Competitive Intelligence
The competitorNamed subtype fires when a target account discusses your competitor on air. Hear how prospects describe rival tools in their own words, then position against the exact objections and preferences they stated publicly.
- 02
Executive Opinion Openers
executiveOpinion signals capture what leaders at target accounts actually believe about their market. Referencing a specific take from a 45-minute episode proves you did real research, not a LinkedIn skim.
- 03
Tech Adoption Detection
techAdoption and aiInvestment subtypes surface when executives mention tools they are using, evaluating, or migrating away from. A CTO announcing a Kubernetes migration on a podcast is a buying signal you will not find in firmographic data.
- 04
Pain Point Discovery
painPointDisclosed signals extract problems executives admit to on air, each scored for intensity. Open your outreach with a pain the prospect described publicly, in their own words, and the pitch writes itself.
- 05
Growth & Expansion Tracking
growthSignal, marketExpansion, and partnership subtypes catch expansion plans discussed in interviews weeks before they hit the press. Early warning on new markets, headcount, and strategic bets.
Measured, Not Estimated
What's actually inside Company Podcast Mention Signals
Volumes, subtypes, geography, and join-key fill rates audited directly from our production database, so you can verify coverage before you buy.
Top subtypes
66 totalGeography
all 67 countries →Top industries
all industries →Join keys
fill rateSee the real records for yourself.
See full coverage & segmentation across all 35 signal types →
Worked Example
One signal, traced to outcome
One Company Podcast Mention Signals record followed from detection, through the action a rep took on it, to the measured result. Illustrative composite of observed customer workflows.
01 signal → 02 action → 03 outcome
Signal detected
The CTO of a target account joins a developer podcast and spends ten minutes explaining why their observability costs have spiraled and why they are re-evaluating their monitoring stack. An executiveOpinion signal with a painPointDisclosed companion fires within the monthly refresh.
Action taken
Your AE opens outreach to the VP of Engineering with the episode:
“Caught your CTO on Software Engineering Daily saying observability spend had gotten out of hand. That is the exact problem we built for, worth 15 minutes?”
Outcome
4x higher reply rate
Because the message referenced a pain point the executive stated publicly, in their own words, weeks before any competitor noticed.
FAQ
Frequently Asked Questions
How are company podcast signals different from contact-level podcast signals?
How fresh are company podcast signals and what volume should I expect?
What intelligence is extracted from each episode?
How It Works
From Raw Data to Your Stack
Autobound ingests from LinkedIn API, Glassdoor, GitHub, Reddit, G2, extracts structured signals with AI, and delivers them however your infrastructure needs.
Autobound Ingests
Raw data from LinkedIn API, Glassdoor, GitHub, Reddit, G2 is continuously collected and normalized across millions of sources.
AI Extracts & Scores
ML models extract 10 signal subtypes with relevance scoring, confidence levels, and entity resolution.
You Receive
Structured JSONL delivered via your preferred method, updated on a monthly cadence.
REST API
Real-time access with subtype filtering
300 req/minGCS Push
Automated delivery to your bucket
JSONL + ParquetEnrich API
On-demand LLM-ranked insights
AI relevance scoringFlat File
Bulk exports for data warehouses
CSV, JSON, Parquet“By consolidating three data vendors into Autobound's Enrich API, we added 100+ new signal types and saved 4 months of engineering time.”
AiSDR Team
Engineering, AiSDR
Ready to License Company Podcast Mention Signals?
Custom pricing based on signal types, delivery frequency, and volume. Full schema documentation and integration guides included.