watt

𝗗𝗮𝘁𝗮 𝗶𝘀 𝘁𝗵𝗲 𝘁𝗵𝗶𝗿𝗱 𝘂𝘁𝗶𝗹𝗶𝘁𝘆. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗪𝗮𝘁𝘁.

For seven months, we've been working on something we believe is foundational to the next era of AI.

AI agents are making consequential decisions (who to target, whether to approve, how to price). And they're doing it blind.

Not because the data doesn't exist. Because the infrastructure to make it available to agents never existed. Every data system ever built was designed for humans querying databases. Agents don't query. They reason. The entire data layer had to be rebuilt from the ground up.

So we built it.

Watt is the reasoning layer for people and company data, purpose-built for AI agents. 82,000+ real-world behavioral signals. 15 trillion data points. Time to agent: minutes.

Real-world behavioral signals synthesized into a graph your agents can actually traverse.

Our team has 50+ years of combined experience building infrastructure for the most complex data use cases in the world (think: hedge funds, NASA).

We're already in production with numerous customers who are building on Watt today. And we're scaling fast.

If you're building AI agents that need to understand the real world, please reach out. We'd love to show you what's possible.

And if you want to help build the data infrastructure layer for the agentic era: We're hiring.

Come find us. We're just getting started.

Introducing Market Fingerprint: what a city does differently, in data. No. 01 is Wichita, closest to the geographic center of the US. 325,055 people across 21 ZIP codes vs. the rest of the country. The pattern: machines, and the people who keep them running (love their sports teams).

- Wichita State · 5.86×
- Emergency HVAC repair · 3.12×
- Distributed energy · 2.55×
- Wind power · 2.51×
- Boeing 737 · 1.71×

Real behavior from 250M+ US adults, recomputed daily.

Which city next?

What is the Signal Graph?

An agent can't reason over petabytes of raw data. Not because it's too slow, but because there's nothing to grip.

Chief Architect John Zila on the substrate behind Watt:

→ A signal is a boolean clause. The graph stores, for every entity, whether that clause holds.
→ Signals are the asset. The graph holds about 162,000 signals; compositions over them generate more audiences than anyone could enumerate.
→ The answers are already on the shelf. For every signal, the exact set of entities is precomputed. Composition is an intersection of signals.
→ The graph traverses, not just filters. A single edge (eg, who employs whom) lets a composition cross from people to their companies and back.

A growing library of signals. An infinite space of questions. That's the substrate.

Signal Stack 01 is live.

A new series, breaking down cultural moments through behavioral data. First up: 𝗪𝗵𝗼 𝗮𝗿𝗲 𝗔𝗺𝗲𝗿𝗶𝗰𝗮'𝘀 𝘀𝗼𝗰𝗰𝗲𝗿 𝗳𝗮𝗻𝘀?

We built an audience of ~3.1M avid U.S. fans from 145,000+ behavioral signals. What stood out:

→ Italian ancestry over-indexes at 1.19×, Hispanic and Latino background at 1.11×. The rest of the demographic picture reads ordinary.
→ Fans concentrate in the Northeast and Florida. California sits well below its population share. The current USMNT roster maps the same corridor: Pulisic, Adams, and roughly half the squad have Northeast roots.
→ Interests cluster around upscale-leisure: tennis, hockey, international travel.
→ Credit and income signals lift slightly. Retail spending falls.

Signal Stack runs every two weeks. See you in two.

Signal Drop 01 is live.

Every two weeks we ship new signals into the Signal Graph and tell you exactly what landed. This one's a big drop.
→ Education signals on 118M+ people. "Alumni of X who are in-market right now" is a one-line query.
→ Geo as a composable signal. State, county, zip, MSA, CBSA, congressional district — mix them, stack them, reason across them like any other signal.
→ Company founding year, LinkedIn contacts, work-vs-personal flags on every email and phone, mobile ad IDs, and 1,700+ new intent topics.

More signals, all reasoned over the same way, all composable in plain language.

See you in two weeks. Link in comments.