Conference Speaker Intelligence

Conference Speaker Intelligence identifies individuals who presented at tech conferences, providing actionable B2B signals about which companies are investing in developer relations, thought leadership, and community engagement. A company sending engineers to speak at KubeCon or SmashingConf is actively building in that domain, has event budget, and is signaling technical direction.

We aggregate conferences monthly from confs.tech and developers.events, covering 18+ technology topics and ~5,400 events/year. Each speaker is extracted via structured feeds (Sessionize) or agentic LLM crawling, then enriched with company domain resolution and contact data from our Salutary dataset.

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See real delivered data → Sample Files

This signal has a single subtype — every signal represents someone who actually presented at a conference.

Signal	Subtype Enum	Description
Conference Speaker	`conferenceSpeaker`	Individual who presented at a tech conference

Example Signal

What a single entry looks like in a delivered signal file:

{
  "signal_id": "confspeaker-smashingconf-amsterdam-tj-pitre",
  "batch_id": "2026-04-29-00-00-00",
  "signal_type": "conference-speaker",
  "signal_subtype": "conferenceSpeaker",
  "association": "contact",
  "detected_at": "2026-04-29T16:55:47Z",
  "company": {
    "name": "Southleft",
    "domain": "southleft.com",                   // match on domain
    "linkedin_url": "linkedin.com/company/southleft",  // or match on LinkedIn URL
    "industries": ["Design", "Software Development"],
    "employee_count_low": 11,
    "employee_count_high": 50,
    "description": "Front-end design and development agency..."
  },
  "contact": {
    "name": "TJ Pitre",
    "first_name": "TJ",
    "last_name": "Pitre",
    "email": "[email protected]",              // match on email
    "job_title": "Founder and CEO",
    "linkedin_url": "linkedin.com/in/tpitre"  // or match on LinkedIn URL
  },
  "data": {
    "summary": "TJ Pitre (Founder and CEO, Southleft) at SmashingConf Amsterdam 2026.",
    "conference_name": "SmashingConf Amsterdam 2026",
    "conference_url": "https://smashingconf.com/amsterdam-2026",
    "conference_date": "2026-04-13",
    "conference_end_date": "2026-04-16",
    "conference_location": "Amsterdam, Netherlands",
    "conference_topics": ["UX", "design", "CSS", "accessibility", "design systems", "AI", "front-end"],
    "conference_estimated_size": "550",
    "conference_description": "SmashingConf Amsterdam 2026 is a conference for front-end developers and designers focused on UX, CSS, accessibility, and AI.",
    "is_future_event": false,
    "session_title": "AI Without the Chaos: Context-Based Design Systems in Practice",
    "session_description": "AI is powerful, fast, and exceptionally good at exposing cracks in how teams design and build products. Without structure, it amplifies confusion...",
    "session_type": "talk",
    "session_track": "Design Systems",
    "session_recording_url": null,
    "co_speakers": null,
    "speaker_bio": "TJ Pitre is the Founder and CEO of Southleft, a front-end design and development agency specializing in design systems...",
    "relevance": 0.82,                       // 0.0-1.0; higher = more actionable for outreach
    "confidence": "high",                    // how certain this signal is accurate
    "extraction_method": "llm-speakers_list+bio",
    "contact_resolved": true,
    "domain_match_score": 10,
    "domain_match_confidence": "high",
    "social_profiles": null,
    "technologies_mentioned": null
  }
}

Field Reference

Standard envelope and entity fields are shared across all signals — see Schema and Resolution. The fields below are specific to this signal:

Signal-Specific Fields

The data object contains everything unique to this signal type — the intelligence extracted from the conference schedule.

Field	Type	Description
`summary`	string	One-line headline: speaker, title, company and conference. Designed to be shown directly to end users
`conference_name`	string	Full conference name including year and location. Useful for display and deduplication
`conference_url`	string	Conference website URL. Useful for fact-checking or sharing with reps
`conference_date`	string (YYYY-MM-DD)	Event start date. Use for recency filtering
`conference_end_date`	string (YYYY-MM-DD)	Event end date
`conference_location`	string	Event location (city, country). Useful for territory-based routing
`conference_topics`	array[string] \| null	Conference topic tags. Useful for filtering by technology domain or sales motion
`conference_estimated_size`	string \| null	Expected attendance. Larger events = stronger signal of investment
`conference_description`	string \| null	AI-generated description of the conference
`is_future_event`	boolean	`true` when the conference has not happened yet at delivery time
`session_title`	string \| null	Title of the speaker's talk. Often reveals the specific technology or problem area they're focused on
`session_description`	string \| null	Talk abstract or description. Contains the richest detail about what the speaker is working on and thinking about
`session_type`	string \| null	One of: `keynote`, `talk`, `workshop`, `panel`, `lightning_talk`, `fireside_chat`, `tutorial`. Keynotes signal higher seniority and investment
`session_track`	string \| null	Conference track or category
`session_recording_url`	string \| null	Link to recorded talk (YouTube, etc.). Populated after event when available
`co_speakers`	array[object] \| null	Other speakers in the same session
`co_speakers[].name`	string	Co-speaker name
`co_speakers[].company`	string \| null	Co-speaker company
`co_speakers[].company_domain`	string \| null	Co-speaker company domain
`co_speakers[].title`	string \| null	Co-speaker job title
`co_speakers[].email`	string \| null	Co-speaker email. Currently always `null`
`co_speakers[].linkedin_url`	string \| null	Co-speaker LinkedIn. Currently always `null`
`speaker_bio`	string \| null	Speaker biography from conference site. Often contains company context and technical focus areas
`relevance`	float (0–1)	Composite B2B relevance score. Weighted by session type (40%), conference size (30%), solo speaker (15%), and contact resolution (15%)
`confidence`	string	`high`, `medium`, or `low`. How certain we are the extraction is accurate
`extraction_method`	string	How speaker data was extracted (e.g., `sessionize`, `llm-speakers_list+bio`). Useful for debugging
`contact_resolved`	boolean	`true` when we matched the speaker's company to a domain (same as `domain_match_score` = 10). It does not mean an email is attached: check `contact.email`
`domain_match_score`	integer	Company domain match score: `10` = matched, `0` = no match
`domain_match_confidence`	string \| null	`high` when the company domain matched. `null` when there is no match
`social_profiles`	null	Reserved. Currently always `null`
`technologies_mentioned`	null	Reserved. Currently always `null`

Timing & Delivery

detected_at is when we processed the conference schedule. Use conference_date for when the event actually happens.
One signal per speaker per conference. A speaker presenting multiple sessions at the same conference produces one signal (with the highest-relevance session).
Each delivery arrives in a timestamped folder. Treat all signals in a new folder as recent — no need to diff against prior deliveries.

Coverage

Refresh: Monthly (1st of month)
Coverage: 5,400+ conferences/year across 18+ technology topics
Best for: Developer tools sales, devrel-focused companies, event sponsorship targeting, executive outreach

Contact Sales →

See real delivered data → Sample Files

Example Signal

Field Reference

Signal-Specific Fields

Timing & Delivery

Coverage