Podcast Appearances (Contact)

Podcast appearances by individual contacts—what they said, the topics they care about, and where they're building a public voice.

Overview

Podcast Appearances (Contact) capture the moments a prospect goes on the record—what an executive actually said on a podcast, in their own words, attributed to the specific person speaking.

We process episodes across a large catalog of business and industry shows, transcribe them, and extract structured signals tied to the individual contact. Each row carries one of 43 subtypes, the supporting quotes, and our estimate of when the event took place.

There are two row shapes, distinguished by signal_subtype:

  1. Appearance rows — signal_subtype: "podcastAppearance". One row per guest per episode: the host, the topics covered, the occasion, their key talking points (each with a supporting quote), co-guests, and a snapshot of what their company does.
  2. Claim rows — any other subtype (e.g. aiInvestment, fundingEvent, techAdoption, careerHistory). A single quote-backed claim the contact made, usually about their own company. Claims about other companies are delivered as Podcast Mentions (Company).

The data object differs between the two—see Data Object below.

Available Subtypes

The signal_subtype field takes one of the 43 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.

podcastAppearance and careerHistory exist only on this contact type. podcastSponsorship exists only on Podcast Mentions (Company).

Appearance (1)

One row for each guest on each episode.

Subtype EnumDescriptionRows delivered
podcastAppearancePerson appeared as a guest on a podcast episode20,948

Themes (20)

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

Subtype EnumDescriptionRows delivered
aiInvestmentCompany invests in AI models, tools or AI features2,353
dataInvestmentCompany invests in data infrastructure, analytics or data quality817
cybersecurityInvestmentCompany invests in security tools, teams or programs151
cloudInvestmentCompany invests in cloud infrastructure or a cloud migration12
automationInvestmentCompany invests in process or workflow automation496
digitalTransformationCompany runs a broad digital-transformation program371
legacyModernizationCompany replaces or modernizes legacy systems159
techInvestmentCompany invests in technology that no narrower theme covers873
customerExperienceFocusCompany makes customer experience a stated priority1,557
efficiencyFocusCompany makes operational efficiency or productivity a stated priority930
costReductionCompany cuts costs or runs a cost-reduction program151
marketExpansionCompany moves into new markets, segments or verticals2,408
internationalGrowthCompany expands into new countries or regions219
platformStrategyCompany builds a platform or ecosystem strategy1,185
strategyShiftCompany changes direction, business model or focus1,943
talentChallengeCompany struggles to hire or keep talent226
supplyChainPainCompany has supply-chain or sourcing problems11
scalingChallengeCompany struggles to scale operations, systems or teams44
complianceBurdenCompany carries a regulatory or compliance burden92
strategicInitiativeCompany names a strategic initiative or company-level priority2,854

Knowledge (8)

Facts about the stack, buying behavior and opinions.

Subtype EnumDescriptionRows delivered
painPointDisclosedOperational pain voiced by someone at the company2,770
techAdoptionNamed tool or vendor currently in the stack3,073
buyingIntentCompany is actively evaluating a solution now76
buyingCriteriaHow they select vendors (build-vs-buy, budget rules)1,134
churnRiskDissatisfaction with, or migration away from, a named vendor40
executiveOpinionA stance strong enough to warrant a row260
industryPredictionInvestor or analyst thesis about the company or its market506
hiringSignalHiring plans or hiring challenges at the company806

Events (11)

First-party disclosures: the contact states an event at their own company.

Subtype EnumDescriptionRows delivered
fundingEventCompany raised or is raising a funding round421
acquisitionAnnouncedCompany announced an agreement or plan to acquire another company71
acquisitionCompletedCompany completed an acquisition326
productLaunchCompany launched or will launch a product or service2,195
partnershipCompany has a partnership or alliance with a named company1,373
expansionCompany adds offices, facilities, locations or capacity807
leadershipChangeCompany has a new executive or an executive departure307
layoffsCompany reduced or will reduce headcount18
majorContractWinCompany won a large contract or customer304
cybersecurityIncidentCompany had a breach or other security incident5
achievesCertificationCompany earned a certification or compliance standard (for example SOC 2 or ISO)100

Durable history (3)

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

Subtype EnumDescriptionRows delivered
careerHistoryGuest's track record: exits, companies founded or sold, well-known past clients10,560
formerVendorCompany used a named vendor and left (displacement intel)375
formerClientCompany previously served a named client66

Schema — Appearance row (podcastAppearance)

{
  "signal_id": "b47a9eb9-2b2f-5e75-865a-03409f2effa2",
  "batch_id": "2026-09-20",
  "signal_type": "podcast-contact",
  "signal_subtype": "podcastAppearance",
  "signal_name": "Tom McKenna (Global Head of Media Operations and Business Development @ Audible) on Strictly Business",
  "detected_at": "2026-09-20T15:05:23Z",
  "association": "contact",
  "company": {
    "name": "Audible",
    "domain": "audible.com",
    "linkedin_url": "linkedin.com/company/audible",
    "industries": [
      "Software Development"
    ],
    "employee_count_low": null,
    "employee_count_high": null,
    "description": "Audible provides a platform for accessing audio-based content."
  },
  "contact": {
    "full_name": "Tom McKenna",
    "first_name": "Tom",
    "last_name": "Mckenna",
    "job_title": "Svp, Global Head of Media, Ops & Business Development (L8)",
    "email": "[email protected]",
    "linkedin_url": "https://www.linkedin.com/in/mckennatom"
  },
  "data": {
    "episode_id": "60588532285",
    "podcast_name": "Strictly Business",
    "episode_title": "The Future of Media Buying: Turning Buzz into Business With WPP Media’s Nancy Hall and Audible’s Tom McKenna",
    "episode_url": "https://omny.fm/shows/strictly-business-1/the-future-of-media-buying-turning-buzz-into-business-with-wpp-media-s-nancy-hall-and-audible-s-tom-mckenna",
    "transcript_source": "transcript",
    "headline": "Tom McKenna (Global Head of Media Operations and Business Development @ Audible) on Strictly Business",
    "evidence": null,
    "is_ad": false,
    "published_at": "2026-09-18",
    "recorded_at_estimate": "2026-09-17",
    "date_confidence": "exact",
    "occasion": "Speaking at Variety's Entertainment and Technology Summit",
    "host": "Cynthia Littleton",
    "company_snapshot": {
      "what_they_do": "Audiobooks and audio entertainment",
      "org_facts": [],
      "technologies_used": [
        "Dolby Atmos"
      ],
      "customers_mentioned": [
        "British Airways",
        "JetBlue",
        "Twitch",
        "NBC Universal"
      ],
      "competitors_mentioned": []
    },
    "topics": [
      "Media strategy",
      "Content formats",
      "Community engagement",
      "Brand campaigns"
    ],
    "talking_points": [
      {
        "point": "Full-cast, cinematic audio productions are performing well.",
        "quote": "full cast cinematic audio productions tend to work really, really well. We're seeing that resonate. It about the immersive sound design It about the Dolby Atmos the 360 degree sound We seeing that work really really well."
      },
      {
        "point": "Creator-led storytelling provides authentic promotion.",
        "quote": "We also seeing creator storytelling tend to work really really well for us So think about like hearing it naturally in a voice... it comes from them, their own voice, their own fan and their own influencer group."
      },
      {
        "point": "Building fandom requires creating exclusive, behind-the-scenes access.",
        "quote": "there's an insider status that I think starts to become really popular here. You've got to do that. Behind the scenes, clips, exclusive content. So making of videos tends to work really, really well."
      }
    ]
  }
}

Schema — Claim row (e.g. productLaunch)

{
  "signal_id": "7cdccdd0-c203-5147-b57e-693051dee800",
  "batch_id": "2026-09-20",
  "signal_type": "podcast-contact",
  "signal_subtype": "productLaunch",
  "signal_name": "Brex · productLaunch · completed (2026-09)",
  "detected_at": "2026-09-20T15:05:33Z",
  "association": "contact",
  "company": {
    "name": "Brex",
    "domain": "brex.com",
    "linkedin_url": "linkedin.com/company/brexhq",
    "industries": [
      "Financial Services"
    ],
    "employee_count_low": null,
    "employee_count_high": null,
    "description": "Brex provides a financial platform that integrates corporate charge cards, cash management accounts, and expense and travel management tools."
  },
  "contact": {
    "full_name": "Pedro Franceschi",
    "first_name": "Pedro",
    "last_name": "Franceschi",
    "job_title": "Founder and Chief Executive Officer",
    "email": "[email protected]",
    "linkedin_url": "https://www.linkedin.com/in/pfranceschi"
  },
  "data": {
    "episode_id": "60341295305",
    "podcast_name": "Behind the Craft",
    "episode_title": "Stop Building AI Agents. Build AI Employees Instead (Live Demo) | Pedro Franceschi",
    "episode_url": "https://podcasters.spotify.com/pod/show/peter-yang42/episodes/Stop-Building-AI-Agents--Build-AI-Employees-Instead-Live-Demo--Pedro-Franceschi-e3ond3v",
    "transcript_source": "transcript",
    "headline": "Brex · productLaunch · completed (2026-09)",
    "evidence": "that's where we build Crab Trap. So the idea of Crab Trap and actually I can just show you the GitHub repo. Yeah, Crab Trap is open source, right? So any company can do it. Yeah, so Crab Trap is open source and really what it does is it's like proxy that intercepts requests.",
    "is_ad": false,
    "published_at": "2026-09-13",
    "recorded_at_estimate": "2026-09",
    "date_confidence": "inferred",
    "quotes": [
      "that's where we build Crab Trap. So the idea of Crab Trap and actually I can just show you the GitHub repo. Yeah, Crab Trap is open source, right? So any company can do it. Yeah, so Crab Trap is open source and really what it does is it's like proxy that intercepts requests."
    ],
    "speaker": "Pedro Franceschi",
    "speaker_relation": "own_company",
    "timing": "completed",
    "event_date": "2026-09"
  }
}

Field Reference

Core Fields

FieldTypeRequiredDescription
signal_idstring (UUID)✓Unique, deterministic identifier for this signal (the same fact always gets the same ID)
batch_idstring (date)Weekly processing batch that produced the row (YYYY-MM-DD). Present in GCS bucket files; API responses can omit it
signal_typestring✓Always "podcast-contact"
signal_subtypestring✓podcastAppearance or a claim subtype (see allowed values above)
signal_namestring✓Human-readable label (same value as data.headline). Appearance rows: Name (Title @ Company) on Show. Claim rows: Company · subtype · timing (event_date)
detected_atstring (ISO 8601)✓When Autobound detected/ingested the signal (not the episode date—see data.published_at)
associationstring✓Entity association type—always "contact"
companyobject✓The contact's company (see Company Object)
contactobject✓The individual the signal is about (see Contact Object)

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

Company Object

FieldTypeRequiredDescription
company.namestring✓The contact's company
company.domainstring✓Company domain (primary match key)
company.linkedin_urlstringCompany LinkedIn URL without scheme (e.g. linkedin.com/company/brexhq); nullable
company.industriesarray[string]Industry classifications; nullable
company.employee_count_lowintegerLower bound of employee-count range; nullable
company.employee_count_highintegerUpper bound of employee-count range; nullable
company.descriptionstringCompany description; nullable

Contact Object

FieldTypeRequiredDescription
contact.full_namestring✓Full name of the contact
contact.first_namestringFirst name
contact.last_namestringLast name; nullable
contact.job_titlestringJob title on record; nullable. It can differ from the title stated on the episode, which appears in signal_name on appearance rows
contact.emailstringWork email (match key); nullable
contact.linkedin_urlstring (URL)✓Contact LinkedIn profile URL (match key)

Data Object

Fields on every row:

FieldTypeRequiredDescription
data.headlinestring✓One-line summary of the signal (same value as signal_name)
data.is_adboolean✓Always false on contact rows (sponsorships are company-only)
data.podcast_namestring✓Name of the podcast show
data.episode_titlestring✓Title of the episode
data.episode_urlstring (URL)Link to the episode; nullable
data.episode_idstring✓Source podcast episode identifier
data.published_atstring (date)✓Episode publication date (YYYY-MM-DD)
data.recorded_at_estimatestring✓Best estimate of when the episode was recorded (YYYY-MM or YYYY-MM-DD)
data.date_confidencestring✓Confidence in the date fields: exact, inferred or unknown
data.transcript_sourcestring✓Origin of the analyzed text (currently always transcript)
data.evidencestringPrimary supporting quote (the first entry of quotes); always null on appearance rows

Fields present only on appearance rows (podcastAppearance). Each can be absent when the episode does not supply it:

FieldTypeDescription
data.hoststringPodcast host name
data.occasionstringThe occasion / context of the appearance
data.co_guestsarray[object]Other guests on the same episode
data.co_guests[].namestringCo-guest name
data.co_guests[].titlestringCo-guest title as stated on the episode
data.co_guests[].employerstringCo-guest employer as stated on the episode
data.topicsarray[string]High-level topics discussed in the episode
data.talking_pointsarray[object]Key points the contact made (see below)
data.talking_points[].pointstringParaphrased key point
data.talking_points[].quotestringSupporting verbatim quote
data.company_snapshotobjectContext about the contact's company from the episode
data.company_snapshot.what_they_dostringWhat the company does, in the speaker's words (nullable)
data.company_snapshot.org_factsarray[string]Stated numbers and facts about the company
data.company_snapshot.technologies_usedarray[string]Tools and vendors mentioned as in use
data.company_snapshot.customers_mentionedarray[string]Customers named on the episode
data.company_snapshot.competitors_mentionedarray[string]Competitors named on the episode

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

FieldTypeDescription
data.speakerstringName of the person who made the statement
data.speaker_relationstringSpeaker's relationship to the company: own_company (the usual value) or third_party; can be absent
data.quotesarray[string]Verbatim transcript quotes supporting the signal
data.timingstringWhen 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_datestringBest 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_textstringRaw temporal phrase from the transcript (e.g. "October 9th"), when present

Coverage

  • Bucket: gs://autobound-podcast-contact/
  • Refresh: Weekly
  • Association: Contact-level
  • Subtypes: 43 (podcastAppearance + 42 claim subtypes)

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