Mission in
You describe the ambitious outcome the organization should pursue, not the steps every agent should take.
BigNumberTheory is live today as context sharing
Build the future where humans and AI work together
SimpleGen is building BigNumberTheory: a platform that turns a mission, people, agents, and resources into an edge-optimized graph for more ambitious work. Today's BNT is the live context layer that lets graph edges pass the right context forward.
Product vision
AI should expand what people can become.
The easy story says AI replaces humans one task at a time. We believe the better future is more ambitious: humans and AI working together toward goals neither could reach alone.
In that future, humans do not disappear from the work. They set direction, taste, judgment, and trust while AI absorbs repetitive coordination, remembers context, routes handoffs, and helps people move into roles that are more creative, satisfying, and rewarding.
BigNumberTheory is the next layer for that world: a computational graph for human and AI work. The nodes are people, agents, and resources selected by capability and availability. The edges carry context, outputs, decisions, and feedback toward the mission.
BNT is not a central brain that gathers every result and draws a master trajectory. It operates the graph itself: optimizing which edges should exist, what context should pass across each edge, and how the graph should adapt after every run.
Edges activate
BNT passes the right context between people, agents, and resources instead of collecting work into a hub.
Graph optimizes
Results and feedback improve the graph structure, routing, and context flow from run to run.
Live foundation
Today's bignumbertheory.com is the context layer this graph builds on.
The full org graph is in development. The live product at bignumbertheory.com already extracts what agents learn at work, shares that context across sessions and across agents, and improves retrieval from feedback.
That matters because a self-driving organization needs more than isolated agents. It needs memory, permissions, routing, and a way to get better from every run.
2,893
experiences mined from real agent work
4,344
retrievals into agent sessions
1,400+
cross-agent context handoffs
45
production iterations of the matching loop
Where it starts
Start with the people already pushing agents beyond one-off chats.
SimpleGen is building for founders, individual power users, and small AI-native teams first: the people who need an organization before they can staff one, and who already feel the cost of driving every agent by hand. The goal is not to remove the human from the loop. It is to give the human a more powerful loop to lead.
Founders
For people with a company mission bigger than the staff they can hire today.
Agent power users
For builders already running Claude Code, Codex, and other agents by hand.
AI-native teams
For teams that need shared memory, cleaner handoffs, and a path toward autonomous coordination.