TARIFF· 4–8 WK · HIGH
US imposes 25% tariff on Chinese EVs
CN EV exports ↓ → MX contract manufacturing utilization ↑, US used-EV prices soften
AGENTIX · DECISION INFRASTRUCTURE
Agentix builds WOMO: the enterprise world model.
Every organization holds a model of the world and a model of itself, and the two never touch. WOMO holds both, and the causal path between them. Deployed inside your perimeter, where your data never leaves.
THE IDEA · 2 MINUTES
Every system you own is a system of record. It can tell you what happened. WOMO is a system of foresight: a live model of your entity and its world, run forward.
THE QUESTION NOTHING ELSE ANSWERS
Freight costs spike thirty percent. Every system in the building can tell you that happened, and several will give you a sentiment reading on it. None of them can tell you what it does to this company's cash position and covenant headroom over the next eighteen months. That path is the product.
WOMO, LIVE
SIMULATION REPLAY · 194K CANONICAL ENTITIES · 119K VALIDATED CAUSAL EDGES
GLOBAL SIGNAL GRAPHUPDATED CONTINUOUSLY
GREEN POSITIVE · RED NEGATIVE · GRAY NEUTRAL
ENERGY· 2–5 WK · HIGH
OPEC+ announces surprise 1.2M bpd supply cut
Brent ↑ → IN refining margins compress, DE chemicals feedstock costs ↑
SUPPLY CHAIN· 8–12 WK · MODERATE
Sony shifts image-sensor production to Hanoi
VN electronics FDI ↑ → CN sensor makers cut prices, smartphone BOM deflation
SIGNALS/S
284.0K
EVENTS/24H
128
COUNTRIES
47
AVG LATENCY
3.2S
MIRAGE: the benchmark for forward simulation
Existing benchmarks grade how convincing output looks; MIRAGE measures coherence: whether a system holds one consistent internal world. Protocol, anti-contamination design, and reference baselines published at Womo Labs; frontier results next.
SEE THE METHODOLOGY →Three systems. One model: WOMO.
SUBSTRATE / THE WORLD MODEL (WOMO)
ONE REPRESENTATION OF YOUR MARKET, NOT A LIBRARY OF DOCUMENTS
119K+ VALIDATED CAUSAL EDGES · 194K+ CANONICAL ENTITIES
LEARNING / ZERO-FORGET CONTINUAL LEARNING
THE MODEL DOES NOT GET WORSE AT WHAT IT ALREADY KNEW
CONTINUAL LEARNING, NOT CONTINUAL RETRAINING: THE MODEL UPDATES IN PLACE, WITHOUT FULL RETRAINS.
BACKWARD TRANSFER ≈ 0%
REASONING / CAUSAL INFERENCE ENGINE
SHOWS THE PATH, NOT JUST THE ANSWER
GROUNDED IN DO-CALCULUS
Sovereign by architecture, not by policy.
Others ask you to send data to their models. We send the model to your data.
The platform runs entirely inside your virtual private cloud: no call home, no shared tenancy, no data egress by construction. Autonomy is governed: every threshold, every action boundary, and every escalation path is set by you and auditable by you. Deployable in-region, air-gapped where required, with no hyperscaler dependency.
- SOVEREIGN VPC
- ZERO DATA EGRESS
- GOVERNED AUTONOMY
- 12 PATENTS FILED
YOUR PERIMETER
MODEL
DATA
INFERENCE
NO EGRESS ACROSS THIS LINE
Demand is not the constraint. Sequence is the decision.
DESIGN PARTNERSHIPS OPENING IN PRIVATE MARKETS · 12 PATENTS FILED · 5 FOUNDATIONAL PAPERS · NEW YORK
WOMO LABS · THE RESEARCH PROGRAM BEHIND WOMO
Five foundational papers spanning world models, continual learning, causal inference, grounded simulation, and black-swan intelligence. Abstracts public; deep-dive materials under briefing.
Visit Womo LabsThe company
FIVE PRIOR EXITS ACROSS THE FOUNDING TEAM · 12 PATENTS FILED · MICROSOFT, GOOGLE, AND STANFORD/CMU/NYU/COLUMBIA ROOTS
FOUNDER & CEO
Ex-Microsoft
Led the team behind Microsoft's first enterprise Copilots and AI agents across a $50B portfolio. Member of the Microsoft AI Council. Prior data-center compute infrastructure startup backed by Cisco, Fidelity, and Nokia, exited to NTT Comm. Now building WOMO, the first true Enterprise World Model.
- 12 PATENTS
- MICROSOFT AI COUNCIL
- EXIT → NTT COMM
CTO
Stanford · ML platforms
Three decades building machine-learning platforms and infrastructure, including engineering leadership at two industry-defining ML platform companies (Gartner Magic Quadrant Visionary). Stanford MS, computer architecture. Three prior exits.
- STANFORD MS
- 3 EXITS
- ML PLATFORMS AT SCALE
FOUNDING TECHNOLOGIST
CMU PhD, ex-Google
Scaled a vertical-AI company from 3 to 200 engineers through a $700M+ acquisition. Led AI engineering at Google across ads forecasting and monetization. CMU PhD in statistical machine learning; 20+ research papers. First investor in the company.
- CMU PHD
- EX-GOOGLE
- $700M+ EXIT
- 3→200 ENGINEERS
FOUNDING RESEARCHER
NYU deep learning group
World-model researcher from NYU's deep learning group; prior exited founder. Research focus: world models, benchmark design, and model evaluation. 15 papers, ~100 citations.
- NYU
- EXITED FOUNDER
- 15 PAPERS
- WORLD MODELS
FOUNDING MTS
RL and simulation
Builds the reinforcement-learning training pipelines. Prior simulation-environment work at an a16z-backed AI company, since acquired.
- RL TRAINING PIPELINES
- SIMULATION ENVIRONMENTS
- FOUNDING EXPERIENCE
CHIEF GROWTH OFFICER
Pre-revenue to unicorn
Scaled an enterprise AI company from pre-revenue to unicorn valuation as growth leader: 2,000+ customers acquired, user base grown from zero to 70K globally in under two years. Built the go-to-market machine, commercial ecosystem, and partner operations from scratch. Columbia Business School.
- PRE-REVENUE → UNICORN
- 2,000+ CUSTOMERS
- 0 → 70K USERS
- COLUMBIA MBA
INVESTORS AND ADVISORS
Operators, not tourists
Current and former executives and technologists from Microsoft, Google, Amazon, Salesforce, and Snowflake.
- MICROSOFT
- AMAZON
- SALESFORCE
- SNOWFLAKE
TEAM ALUMNI
INVESTOR & ADVISOR AFFILIATIONS
FULL TEAM BIOS, PATENT FILINGS, AND BENCHMARK METHODOLOGY AVAILABLE IN THE DATA ROOM UNDER NDA.
MORE ON THE COMPANY →See your world before it moves.
REQUEST A BRIEFING
Briefings are run under NDA. Tell us what you need to see and we will scope the session.