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.
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": {
"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.",
"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": [],
"speaker_bio": "TJ Pitre is the Founder and CEO of Southleft, a front-end design and development agency specializing in design systems...",
"relevance_score": 0.82, // 0.0-1.0; higher = more actionable for outreach
"extraction_method": "llm-speakers_list+bio",
"extraction_confidence": "high", // how certain this signal is accurate
"contact_resolved": true,
"resolution_source": "salutary_2026q2"
}
}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 |
|---|---|---|
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] | Conference topic tags. Useful for filtering by technology domain or sales motion |
conference_estimated_size | string | Expected attendance. Larger events = stronger signal of investment |
conference_description | string | AI-generated description of the conference |
session_title | string | Title of the speaker's talk. Often reveals the specific technology or problem area they're focused on |
session_description | string | Talk abstract or description. Contains the richest detail about what the speaker is working on and thinking about |
session_type | string | 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[].linkedin_url | string | null | Co-speaker LinkedIn |
speaker_bio | string | Speaker biography from conference site. Often contains company context and technical focus areas |
relevance_score | float (0–1) | Composite B2B relevance score. Weighted by session type (40%), conference size (30%), solo speaker (15%), and contact resolution (15%) |
extraction_method | string | How speaker data was extracted (e.g., sessionize, llm-speakers_list+bio). Useful for debugging |
extraction_confidence | string | high, medium, or low. How certain we are the extraction is accurate |
contact_resolved | boolean | Whether contact was matched in our enrichment dataset. true means email and company data are attached |
resolution_source | string | null | Enrichment source (e.g., salutary_2026q2) |
Timing & Delivery
detected_atis when we processed the conference schedule. Useconference_datefor 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
Updated 9 days ago
