SUPER THESIS
The control layer for an agentic economy.
As intelligence becomes cheaper and action becomes abundant, enterprise authority becomes infrastructure.
EXECUTIVE THESIS
Intelligence scales. Authority must scale with it.
Agents need maintained business context to act usefully. But context alone does not answer whether a person, model, or system is authorized to act for this purpose, at this moment, across this sequence.
SUPER connects data-use rights, business purpose, contextual evaluation, observed action sequences, and independent intervention. The initial buyer is the team accountable for deploying enterprise agents safely: security, AI platform, and data owners.
The initial business model assumes implementation revenue, recurring platform contracts, and metered usage. These are commercial assumptions, not reported traction.
WHY AN INDEPENDENT LAYER
Authority should travel across the stack.
Enterprises will use multiple models, data systems and execution tools. An independent layer can preserve one decision lineage across those boundaries rather than restating authority inside every model or application.
Native model, cloud, gateway and application controls remain essential foundations. SUPER’s role is to connect them to enterprise data rights, business purpose and intervention on the covered action path.
HOW THE ADVANTAGE COMPOUNDS
Focus creates reusable operating knowledge.
CURRENT ASSETS
Founder/operator insight and focused access to a consequential partner-data workflow.
NEXT MILESTONES
An expert-reviewed held-out evaluation corpus and repeatable integrations that reduce implementation and review burden.
OVER TIME
Tenant-specific policy lineage, permissioned learning from failure patterns, and operational switching costs as covered workflows expand.
Prospective data and integration advantages are milestones to earn, not a proven network effect.
INVESTMENT MILESTONES
Earn the right to expand.
0–30
Specify the buyer, workflow boundary and evaluation; establish baseline integration cost and review effort.
31–60
Run adversarial and held-out cases in shadow use; measure false holds, operating overhead and reviewer burden.
61–90
Seek paid conversion and repeat purchase only when security gates, integration economics and operating value support it.
BOTTOM-UP MODEL
Planning assumptions, made visible.
A scenario tool—not a forecast, traction claim, or independently established market size. Active customers cannot exceed qualified organizations.
MODELED SERVICEABLE ANNUAL SPEND
$120.0m
ARR SCENARIO
$2.4m
Inputs are bounded: organizations 0–10,000, annual contract $0–$500,000, and active customers 0–2,000.
The larger opportunity may extend from enterprise data sharing into digital and physical task authority; it is intentionally not added to this bottom-up scenario.
EARLY-STAGE INVESTORS + PORTFOLIO DESIGN PARTNERS