Built so any builder can traverse the world's signal — agentically. Your agents don't join tables. They navigate 15 trillion pre-computed relationships in plain language, in sub-second time, at petabyte scale.
Architecture
Watt's data architecture has two layers.
The base graph is a heterogeneous information network. It stores nodes (people, businesses, emails, mobile IDs) connected by edges that represent relationships. Unlike a table, it captures how things relate, not just what they are.
The Signal Graph sits above it. Every day, Watt calculates five types of relationships across the entire base graph: top predictors, top discriminators, top co-occurring, top exclusionary, and top absentee. The result: 15 trillion pre-computed relationship outcomes, recalculated daily.
This is what makes sub-second petabyte-scale queries possible. Your agent doesn't traverse raw data. It traverses a pre-computed map of how everything in the graph relates to everything else.
The Signal Graph
145,000+ signals on people, 55,000+ on businesses, 15 trillion pre-computed relationships — tied to verified identities, traversable by any agent in plain language.
Differentiators
Data isn't stored in flat tables. It lives in a traversable graph. Agents navigate relationships between identities, companies, and signals without upfront schema knowledge. The graph adapts to the query.
A production-hardened Model Context Protocol layer with rate limiting, audit logging, and role-based access. Drop Watt into any agent framework with a single config line. No custom glue code required.
No schema to memorize. No joins to write. Your Signal Engineer asks for what the agent needs and Watt traverses the graph. Discovery, composition, and activation in one query surface.
Data domains
Watt's graph covers signal categories spanning intent, purchase, lifestyle, financial, demographic, employment, political, content, household, affinity, and more — for people, and tech stack, hiring signals, industry, and others for businesses. They're not separate silos. They're intermingled in a single traversable graph.
Name, age, household composition, education, interests, and 12,000+ demographic signals unified across sources. Part of Watt's unified signal graph.
Firmographic data, org charts, funding history, technology stack, and headcount signals for 300M+ companies. Part of Watt's unified signal graph.
Browsing intent, keyword affinity, and research patterns to surface in-market signals in real time. Part of Watt's unified signal graph.
Purchase behavior, spend patterns, category affinities, and wallet-share estimates across retail and digital. Part of Watt's unified signal graph.
Signal categories evolve as new data is onboarded. Use Watt Chat to discover what's available for your use case.
Entity Roadmap
DMV data already acquired. Household and location entities next.
Provenance
Watt was founded by the engineers behind petabyte-scale reasoning systems at some of the world's most demanding institutions. The same non-deterministic reasoning challenges that make LLMs hard are the ones we've been solving for fifteen years.
Every vertical, the same Signal Graph
Expose returners and resellers in your customer base. Create blocklists from chargeback signals. Segment orders into trust tiers at checkout.
Surface income, spend, and financial stress signals for any applicant list. Generate pre-qualified prospects from life event triggers. Rank leads by LTV and default risk, not just credit score.
Score inbound applications into risk tiers based on behavioral anomalies across devices, geography, and identity.
Data minimization and purpose limitation baked in at the architecture level.
Dedicated legal team specializing in data privacy, CCPA, and GDPR compliance.
Clear acceptable use policies with enterprise MSA available on request.
We'll map your agent architecture against the Signal Graph and show you what one Signal Engineer can ship.
