Platform Architecture
How we build signal data
From raw public data to structured, enriched intelligence. Six pipeline stages. 35+ sources. One canonical schema.
01
Ingest
02
Extract
03
Normalize
04
Enrich
05
Resolve
06
Deliver
The signal pipeline
Six stages from raw public data to delivery-ready intelligence.
Ingest
We monitor 35+ public data sources continuously: SEC EDGAR, LinkedIn, job boards, patent databases, Glassdoor, Reddit, GitHub, news feeds, earnings call transcripts, and more. New sources added on a rolling basis — went from 25 to 35+ in five weeks.
Extract
Proprietary AI/LLM models parse unstructured documents — 200-page 10-K filings, podcast transcripts, social posts — into structured signals with typed fields, confidence scores, and entity resolution.
Normalize
Every signal follows a canonical schema regardless of source: signal_id, signal_type, signal_subtype, detected_at, association, company/contact entity, and a structured data payload. Integrate once — every new signal type just works.
Enrich
LLM enrichment adds summaries, pain points (with intensity scores), strategic initiatives (with urgency scores), technologies mentioned (with adoption status), and competitor references. Raw data becomes actionable intelligence.
Resolve
Entity resolution across sources. "Acme AI, Inc." from SEC maps to "acmeai.com" in your CRM. 99%+ domain coverage, 90%+ LinkedIn URL coverage. Cross-source deduplication ensures one clean record per entity.
Deliver
REST API (sub-200ms), GCS push (daily/weekly), flat file (JSONL, Parquet, CSV), or OEM licensing. Pick the delivery method that fits your infrastructure.
35+
Data Sources
250M+
Contact Records
50M+
Company Profiles
<200ms
API Response Time
Frequently Asked Questions
How is this different from intent data providers?
Intent data gives you probability scores based on anonymous browsing behavior from co-ops. We give you verified, timestamped business events with full source attribution. An SEC filing is a fact. A job posting is a fact. Intent is a guess. We do both, but the signal data is what makes us different.
What does the canonical signal schema look like?
Every signal has: signal_id, signal_type, signal_subtype, detected_at, association (company or contact), entity fields (domain, name, LinkedIn URL), and a structured data payload specific to the signal type. Same schema whether it came from an SEC filing or a Reddit post.
How fast are new data sources added?
Our pipeline is built for source velocity. We went from 25 to 35+ sources in five weeks. When we add a new source, it flows through the same extract → normalize → enrich → resolve pipeline. Your integration doesn't change.
What delivery method should I use?
REST API for real-time enrichment and search (sub-200ms). GCS push for batch workflows and warehouse loading. Flat file (JSONL, Parquet, CSV) if you need portable exports. OEM licensing if you're embedding signals into your own product.
How does entity resolution work across sources?
We resolve entities across all 35+ sources using domain matching, fuzzy name matching, and LinkedIn URL resolution. "Acme AI, Inc." from an SEC filing, "acmeai" from a job board, and "acme-ai.com" from a news article all resolve to the same entity. 99%+ domain coverage.
Can I use Autobound signals in my own platform?
Yes. OEM licensing lets you embed our signal data directly into your product with white-label support. Current OEM partners include TechTarget, who embedded our API into Priority Engine. Average time-to-market is 4 weeks.
Start building with signal data
Talk to our team about signal data for your platform.