Frontier results without frontier-only compute.
We build swarms: many efficient models and a few frontier ones, working as peers on hard problems. And we build the system that runs them.
Why swarms
One context window has a ceiling. A swarm doesn't.
A single agent can only hold so much. Summarising its work throws away detail you may need later, and saving notes to disk keeps the facts but loses the understanding.
A swarm of live agents holds many contexts at once, so the size of problem it can take on grows with every agent you add. At frontier-model prices that's out of reach for most teams. Our swarms put efficient models on the broad exploration and save frontier models for the steps that need them.
Mesh, not a tree
Agents that stay alive, talk directly and specialise.
- Persistent agentsAgents keep their context after answering, so they can be asked again instead of starting over.
- Peer coordinationAgents ask whoever holds the context they need, rather than routing everything through one coordinator that becomes the bottleneck.
- Emergent specialistsAn agent that works one corner of the problem becomes the swarm's expert on it.
- Shared recordCandidates, findings and dead ends live in one place every agent can read, with their source and checking status.
- Learned structureHierarchy is added only where it pays for itself, and the swarm's shape adapts to the task.
Run
You bring the problem. The swarm does the search.
Our system coordinates the agents, keeps their messages short and useful, checks their results and tracks every cost.
- A problemAlgorithm development, engineering design, failure-scenario discovery or mathematical search.
- A budgetEnforced across inference, communication, checking and retries.
- Evaluation criteriaTests, simulations or formal checks.
- Your inference keysYour models, orchestrated by us.
Research