Podcast Mentions (Company)

Overview

Podcast Mentions (Company) surface what's being said about a company on podcasts—strategy, stack, funding, product moves, and pain points, pulled straight from the conversation—plus which companies buy ad spots on which shows.

We process episodes across a large catalog of business and industry shows, transcribe them, and extract structured signals about the companies discussed. Each row carries one of 42 subtypes, the verbatim quotes that support it, who said them and how they relate to the company, and our estimate of when the event took place.

There are two row shapes, distinguished by signal_subtype:

Claim rows — any subtype except podcastSponsorship (e.g. techAdoption, fundingEvent, formerVendor). A single quote-backed claim about the company, made by someone who is not speaking about their own employer. (When a guest talks about their own company, the row is delivered as Podcast Appearances (Contact) instead, tied to that person.)
Sponsorship rows — signal_subtype: "podcastSponsorship". The company paid for an ad spot on the show. data.offer_texts holds the ad copy as read, and data.is_ad flags house ads (see below).

Available Subtypes

The signal_subtype field takes one of the 42 values below. The group headings are an organizational grouping for readability only—the group is not a field in the payload. Rows delivered is the number of rows delivered from May 31 to Sep 20, 2026; it shows which subtypes are common and which are rare.

Themes (20)

Priorities and initiatives the speaker voices. The tokens match the theme subtypes on earnings-call and SEC signals.

Subtype Enum	Description	Rows delivered
`aiInvestment`	Company invests in AI models, tools or AI features	145
`dataInvestment`	Company invests in data infrastructure, analytics or data quality	64
`cybersecurityInvestment`	Company invests in security tools, teams or programs	18
`cloudInvestment`	Company invests in cloud infrastructure or a cloud migration	2
`automationInvestment`	Company invests in process or workflow automation	34
`digitalTransformation`	Company runs a broad digital-transformation program	76
`legacyModernization`	Company replaces or modernizes legacy systems	28
`techInvestment`	Company invests in technology that no narrower theme covers	66
`customerExperienceFocus`	Company makes customer experience a stated priority	130
`efficiencyFocus`	Company makes operational efficiency or productivity a stated priority	51
`costReduction`	Company cuts costs or runs a cost-reduction program	17
`marketExpansion`	Company moves into new markets, segments or verticals	159
`internationalGrowth`	Company expands into new countries or regions	7
`platformStrategy`	Company builds a platform or ecosystem strategy	75
`strategyShift`	Company changes direction, business model or focus	168
`talentChallenge`	Company struggles to hire or keep talent	8
`supplyChainPain`	Company has supply-chain or sourcing problems	2
`scalingChallenge`	Company struggles to scale operations, systems or teams	1
`complianceBurden`	Company carries a regulatory or compliance burden	9
`strategicInitiative`	Company names a strategic initiative or company-level priority	458

Knowledge (8)

Facts about the stack, buying behavior and opinions.

Subtype Enum	Description	Rows delivered
`painPointDisclosed`	Operational pain voiced by someone at the company	267
`techAdoption`	Named tool or vendor currently in the stack	1,306
`buyingIntent`	Company is actively evaluating a solution now	12
`buyingCriteria`	How they select vendors (build-vs-buy, budget rules)	99
`churnRisk`	Dissatisfaction with, or migration away from, a named vendor	12
`executiveOpinion`	A stance strong enough to warrant a row	93
`industryPrediction`	Investor or analyst thesis about the company or its market	506
`hiringSignal`	Hiring plans or hiring challenges at the company	34

Events (11)

Company events that someone outside the company (a partner, vendor, customer or commentator) states on air.

Subtype Enum	Description	Rows delivered
`fundingEvent`	Company raised or is raising a funding round	102
`acquisitionAnnounced`	Company announced an agreement or plan to acquire another company	18
`acquisitionCompleted`	Company completed an acquisition	57
`productLaunch`	Company launched or will launch a product or service	212
`partnership`	Company has a partnership or alliance with a named company	250
`expansion`	Company adds offices, facilities, locations or capacity	71
`leadershipChange`	Company has a new executive or an executive departure	57
`layoffs`	Company reduced or will reduce headcount	6
`majorContractWin`	Company won a large contract or customer	75
`cybersecurityIncident`	Company had a breach or other security incident	5
`achievesCertification`	Company earned a certification or compliance standard (for example SOC 2 or ISO)	6

Durable history (2)

Past facts that stay useful. The subtype name marks them as old, so you opt in with a filter.

Subtype Enum	Description	Rows delivered
`formerVendor`	Company used a named vendor and left (displacement intel)	228
`formerClient`	Company previously served a named client	34

Sponsorship (1)

The podcast is the primary source: the company pays for ad spots.

Subtype Enum	Description	Rows delivered
`podcastSponsorship`	Company sponsors a podcast (ad-spend and share-of-voice intel). See `data.is_ad` for house ads	9,659

House ads (`data.is_ad`)

On podcastSponsorship rows, data.is_ad is true when the sponsor is the host or the podcast network promoting itself (a house ad), for example a studio that reads an ad for its own podcast-production service. It is false when a third-party company paid for the spot. On all other subtypes it is always false.

To keep only paid third-party sponsorships, filter signal_subtype = "podcastSponsorship" AND data.is_ad = false. House-ad rows also carry (house ad) at the end of signal_name / data.headline.

Schema — Claim row (e.g. `industryPrediction`)

{
  "signal_id": "abdc69af-8917-53d8-b96c-19825cb08109",
  "batch_id": "2026-09-20",
  "signal_type": "podcast-company",
  "signal_subtype": "industryPrediction",
  "signal_name": "Shopify · industryPrediction · ongoing_state (2026-08)",
  "detected_at": "2026-09-20T15:05:29Z",
  "association": "company",
  "company": {
    "name": "Shopify",
    "domain": "shopify.com",
    "linkedin_url": "linkedin.com/company/shopify",
    "industries": [
      "Retail Apparel And Fashion"
    ],
    "employee_count_low": null,
    "employee_count_high": null,
    "description": "Shopify operates a multinational platform that provides tools for managing retail operations."
  },
  "contact": null,
  "data": {
    "episode_id": "60484451406",
    "podcast_name": "Payments on Fire™",
    "episode_title": "Episode 302 - Catching Up on the Capital Markets, with Timothy Chiodo, UBS",
    "episode_url": "https://glenbrook.com/payments_on_fire/episode-302-catching-up-on-the-capital-markets-with-timothy-chiodo-ubs",
    "transcript_source": "transcript",
    "headline": "Shopify · industryPrediction · ongoing_state (2026-08)",
    "evidence": "ShopPay is moving from what was once more of an SMB product to now, with Shopify's expansion into enterprise that's being quite successful, they're moving into enterprise. So SMB into enterprise, again, something that, similar to Apple Pay, it's basically a doubling of the TAM.",
    "is_ad": false,
    "published_at": "2026-09-16",
    "recorded_at_estimate": "2026-08",
    "date_confidence": "inferred",
    "quotes": [
      "ShopPay is moving from what was once more of an SMB product to now, with Shopify's expansion into enterprise that's being quite successful, they're moving into enterprise. So SMB into enterprise, again, something that, similar to Apple Pay, it's basically a doubling of the TAM."
    ],
    "speaker": "Timothy Chiodo",
    "speaker_relation": "third_party",
    "timing": "ongoing_state",
    "event_date": "2026-08"
  }
}

Schema — Sponsorship row (`podcastSponsorship`)

{
  "signal_id": "0ac6e9a2-4f76-51d0-bb5d-e4e9d4be2bfc",
  "batch_id": "2026-09-20",
  "signal_type": "podcast-company",
  "signal_subtype": "podcastSponsorship",
  "signal_name": "Insurify sponsors The Paul Barron Crypto Show",
  "detected_at": "2026-09-20T15:05:22Z",
  "association": "company",
  "company": {
    "name": "Insurify",
    "domain": "insurify.com",
    "linkedin_url": "linkedin.com/company/insurify",
    "industries": [
      "Software Development"
    ],
    "employee_count_low": null,
    "employee_count_high": null,
    "description": "Insurify is an insurance comparison shopping platform that allows consumers to compare, purchase, and manage various insurance policies."
  },
  "contact": null,
  "data": {
    "episode_id": null,
    "podcast_name": "The Paul Barron Crypto Show",
    "episode_title": "CLARITY Fail Aftermath🔥Coinbase INTERVIEW Kara Calvert🚨",
    "episode_url": "https://www.spreaker.com/episode/clarity-fail-aftermath-coinbase-interview-kara-calvert--75173770",
    "transcript_source": "transcript",
    "headline": "Insurify sponsors The Paul Barron Crypto Show",
    "evidence": null,
    "is_ad": false,
    "published_at": "2026-09-16",
    "recorded_at_estimate": "2026-09-16",
    "date_confidence": "inferred",
    "offer_texts": [
      "Real quotes from more than 120 top insurance companies side by side. About two minutes, free, no phone calls, no pressure... Millions of people have used Insurify to shop for their car insurance. So insure with Insurify. Go to Insurify.com."
    ]
  }
}

Field Reference

Core Fields

Field	Type	Required	Description
`signal_id`	string (UUID)	✓	Unique, deterministic identifier for this signal (the same fact always gets the same ID)
`batch_id`	string (date)		Weekly processing batch that produced the row (`YYYY-MM-DD`). Present in GCS bucket files; API responses can omit it
`signal_type`	string	✓	Always `"podcast-company"`
`signal_subtype`	string	✓	Specific signal subtype (see allowed values above)
`signal_name`	string	✓	Human-readable label (same value as `data.headline`). Claim rows: `Company · subtype · timing (event_date)`. Sponsorship rows: `Company sponsors Show`, plus `(house ad)` when `data.is_ad` is `true`
`detected_at`	string (ISO 8601)	✓	When Autobound detected/ingested the signal (not the episode date—see `data.published_at`)
`association`	string	✓	Entity association type—always `"company"`
`company`	object	✓	The company the signal is about (see Company Object)
`contact`	null	✓	Always `null` for company-level signals

episode_id is nested under data, not at the top level. There is no parent_signal_id field.

Company Object

Field	Type	Required	Description
`company.name`	string	✓	Company discussed or sponsoring
`company.domain`	string	✓	Company domain (primary match key)
`company.linkedin_url`	string		Company LinkedIn URL without scheme (e.g. `linkedin.com/company/shopify`); nullable
`company.industries`	array[string]		Industry classifications; nullable
`company.employee_count_low`	integer		Lower bound of employee-count range; nullable
`company.employee_count_high`	integer		Upper bound of employee-count range; nullable
`company.description`	string		Company description; nullable

Data Object

Fields on every row:

Field	Type	Required	Description
`data.headline`	string	✓	One-line summary of the signal (same value as `signal_name`)
`data.is_ad`	boolean	✓	`true` only on `podcastSponsorship` rows where the sponsor is the host or network promoting itself (house ad). Always `false` on other subtypes
`data.podcast_name`	string	✓	Name of the podcast show
`data.episode_title`	string	✓	Title of the episode
`data.episode_url`	string (URL)		Link to the episode; nullable
`data.episode_id`	string		Source podcast episode identifier. Always `null` on `podcastSponsorship` rows
`data.published_at`	string (date)	✓	Episode publication date (`YYYY-MM-DD`)
`data.recorded_at_estimate`	string	✓	Best estimate of when the episode was recorded (`YYYY-MM` or `YYYY-MM-DD`)
`data.date_confidence`	string	✓	Confidence in the date fields: `exact`, `inferred` or `unknown`
`data.transcript_source`	string	✓	Origin of the analyzed text (currently always `transcript`)
`data.evidence`	string		The primary supporting quote (the first entry of `quotes`). Always `null` on `podcastSponsorship` rows

Fields present only on claim rows (every subtype except podcastSponsorship):

Field	Type	Description
`data.quotes`	array[string]	Verbatim transcript quotes supporting the signal
`data.speaker`	string	Name of the person who made the statement on the episode
`data.speaker_relation`	string	Speaker's relationship to the subject company: `third_party` (someone else discussing it), `counterparty` (a partner, vendor or other party that deals with it), or `own_customer` (the subject company is a customer of the speaker's company). Company rows never carry `own_company`
`data.timing`	string	When the described event occurs relative to the recording: `completed`, `in_progress`, `imminent` (about 60 days), `planned`, `aspirational` or `ongoing_state` (a standing fact, not an event)
`data.event_date`	string	Best estimate of the event date (`YYYY`, `YYYY-MM` or `YYYY-MM-DD`); for `ongoing_state` it is the as-of date. Can be absent
`data.event_date_text`	string	Raw temporal phrase from the transcript (e.g. `"right now"`), when present

Fields present only on sponsorship rows (podcastSponsorship):

Field	Type	Description
`data.offer_texts`	array[string]	The ad copy as read on air, one entry per spot (often includes the sponsor's domain or offer)

Coverage

Bucket: gs://autobound-podcast-company/
Refresh: Weekly
Association: Company-level
Subtypes: 42