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Social Intelligence

Podcast Mention Signals (Company)

Every time a company gets discussed on a B2B podcast - by its own execs or by customers, partners, and competitors - Autobound extracts the business claim, the verbatim quote, and who said it, across 66 subtypes.

8,300+

Company-level signals

~27,700

Companies covered (all podcast signals)

66

Signal subtypes

Weekly

Update frequency

Social Intelligence66 subtypes · Weekly refresh

What Are Company Podcast Mention Signals?

Company podcast mention signals fire when a company, its products, or its market is discussed on a transcribed B2B podcast episode. Each distinct on-air business claim lands as its own signal, classified into 66 subtypes shared with our SEC and earnings vocabulary - aiInvestment, techAdoption, partnership, marketExpansion, painPointDisclosed - plus companyPodcastAppearance for episodes featuring the company itself and sponsorship reads mapped to the sponsoring brand.

The killer field is speaker_relation: it tells you whether the claim came from the company's own team or from a third party. A partner saying 'we're one of their exclusive tech partners and they have 300 facilities' is independent evidence of scale and stack you can't get from a website - and it's a natural door-opener.

Example Signal Subtypes

companyPodcastAppearancetechAdoptionaiInvestmentpartnershippainPointDisclosedmarketExpansionfundingEventexecutiveOpinion

Data Schema

Company Podcast Signal Schema

Real production signal (lightly trimmed): a partner discussing Argus on air - third-party evidence of the company's footprint and tech stack, with the verbatim quote.

podcast_company.schema.json
{
  "signal_id": "4bcf2845-d57d-5480-b390-f5c0dd6321ca",
  "signal_type": "podcast-company",
  "signal_subtype": "techAdoption",
  "signal_name": "Argus Self Storage · techAdoption · ongoing_state (2026-07)",
  "association": "company",
  "detected_at": "2026-08-24T10: 25: 28.000Z",
  "company": {
    "name": "Argus Self Storage",
    "domain": "argus-selfstorage.com",
    "industries": ["Leasing Real Estate"],
    "employee_count_low": 101,
    "employee_count_high": 250,
    "revenue": "25 Million to 50 Million"
  },
  "data": {
    "headline": "Argus Self Storage · techAdoption · ongoing_state (2026-07)",
    "quotes": [
      "we work with some large operators like Argus. We're one of their exclusive tech partners and they have, you know, 200, 300 facilities and we love them."
    ],
    "speaker": "Tommy Nguyen",
    "speaker_relation": "counterparty",  // own_company | counterparty | third_party
    "timing": "ongoing_state",
    "event_date": "2026-07",
    "podcast_name": "The Storage Rebellion",
    "episode_title": "SE2 EPISODE 4: Marketing Is the Easy Part. Building a Great Business Is Hard | Tommy Nguyen (StoragePug)",
    "episode_url": "https://podcasters.spotify.com/pod/show/chris832/episodes/...",
    "episode_id": "57903744797",  // joins all signals from this episode
    "published_at": "2026-07-21",
    "is_ad": false,
    "transcript_source": "transcript"
  }
}
GCS Bucket
gs://autobound-podcast-company/
Formats
JSONL · Parquet
Refresh
Weekly

Applications

What teams build with Podcast Mention Signals (Company)

03 documented applications

  1. 01

    Third-Party Evidence for Account Research

    speaker_relation separates what a company says about itself from what customers, partners, and competitors say about it on air. Independent claims about scale, stack, and momentum are the highest-trust research input your reps can get without a discovery call.

  2. 02

    Catch Buying Conditions as Claims

    Filter to the claim subtypes that map to your trigger events: painPointDisclosed, techMigration, legacyModernization, fundingEvent, expansion. Each one arrives with the verbatim quote, so the outreach writes itself.

  3. 03

    Sponsorship Intelligence

    Ad reads are extracted verbatim and mapped to the sponsoring company. See which brands are spending on which audiences - useful for partner scouting, competitive watch, and media planning.

Measured, Not Estimated

What's actually inside Podcast Mention Signals (Company)

Volumes, subtypes, geography, and join-key fill rates audited directly from our production database, so you can verify coverage before you buy.

Measured coverageaudited from production · minimums, not ceilings
15,000
companies with fresh data / month
basis: published baseline
55,000
records created / month
basis: published baseline

Top subtypes

66 total
Executive Opinion715 · 14%
Product Launch685 · 14%
Company Podcast Appearance482 · 9.7%
Tech Adoption461 · 9.3%
Competitor Named419 · 8.4%
Growth Signal360 · 7.2%
Partnership230 · 4.6%
Industry Prediction168 · 3.4%
🇺🇸United States1.0K · 53%
🇬🇧United Kingdom60 · 3.1%
🇮🇳India52 · 2.7%
🇩🇪Germany41 · 2.1%
🇦🇺Australia37 · 1.9%
🇨🇦Canada34 · 1.8%
🇫🇷France31 · 1.6%
CA 409NY 219TX 100FL 72MA 72IL 49

Top industries

all industries
Software Development194 · 10%
Financial Services104 · 5.4%
Retail56 · 2.9%
Advertising Services56 · 2.9%
IT Services and IT Consulting49 · 2.6%
Leasing Real Estate46 · 2.4%
Higher Education40 · 2.1%

Join keys

fill rate
Domain98%
LinkedIn URL80%
Company name100%
Records measured 4,980+Companies reached 1,914+Full schema & examples →

See the real records for yourself.

Real records from production, delivered to your inbox.

See full coverage & segmentation across all 35 signal types →

Worked Example

One signal, traced to outcome

One Podcast Mention Signals (Company) 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

01

Signal detected

A storage-tech founder tells a podcast host that Argus Self Storage is one of his exclusive tech partners with 200-300 facilities - a third-party claim about Argus's scale and stack.

02

Action taken

Your rep gets the techAdoption signal with the verbatim quote and speaker_relation: counterparty, and opens with the partner's own words.

03

Outcome

The prospect confirms the footprint on the first call - the rep already knew the stack, the scale, and the tone to take.

FAQ

Frequently Asked Questions

How is this different from the contact-level podcast signals?
podcast-contact tracks people: who appeared on which episode and what they said. podcast-company tracks companies: every business claim made about a company on air - by anyone - plus company appearances and sponsorship reads. They join on episode_id.
What does speaker_relation mean?
Who made the claim relative to the company: own_company (their exec said it), counterparty (a customer or partner said it), or third_party (an analyst, competitor, or host said it). Third-party claims are independent evidence you can't get from the company's own materials.
How fresh is the data?
Weekly batches (Sunday), reviewed before publish. Every signal carries published_at, event_date, and a date_confidence flag.
Can I filter to specific subtypes?
Yes - pass signal_subtypes on the search or enrich endpoints (OR semantics, max 25 values, shared with the SEC filing vocabulary). Discover the full list via GET /v1/signals/types.

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.

01

Autobound Ingests

Raw data from LinkedIn API, Glassdoor, GitHub, Reddit, G2 is continuously collected and normalized across millions of sources.

02

AI Extracts & Scores

ML models extract 66 signal subtypes with relevance scoring, confidence levels, and entity resolution.

03

You Receive

Structured JSONL delivered via your preferred method, updated on a weekly cadence.

REST API

Real-time access with subtype filtering

300 req/min

GCS Push

Automated delivery to your bucket

JSONL + Parquet

Enrich API

On-demand LLM-ranked insights

AI relevance scoring

Flat File

Bulk exports for data warehouses

CSV, JSON, Parquet
3 vendors consolidated
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 Podcast Mention Signals (Company)?

Custom pricing based on signal types, delivery frequency, and volume. Full schema documentation and integration guides included.