Green LLM
Cheaper, faster, greener, and it compounds
The industry burns through tokens generating long, formatted outputs
that get parsed right back into structured data. It is wasteful, for your wallet
and for the planet.
Orbix uses a fundamentally different approach. Agents don't produce human-readable
essays. They produce actions. The result is dramatically lower token usage,
faster execution, and reduced compute footprint.
But this is not a one-time saving. Orbix gets greener over time. As your workflows
mature, agents learn shorter paths. Recorded processes replace inference with replay.
Cached rationale means the same decision never needs to be computed twice.
The longer you use it, the less compute it needs.
Day one: less waste. Month six: dramatically less. Year one: a fraction of what legacy systems burn.
API Framework
Input → Action → Rationale
Traditional AI workflows follow Input → Output. The model writes a long response,
your code parses it, then calls an API. Most of that output is waste.
Orbix flips this. The agent receives input, decides the action, and logs its rationale.
There is no verbose output step. The action is the output.
Input
→
Action
→
Rationale
Verbose Output
eliminated
Agent Council, support on every output
No single agent ships alone. Before any output reaches the user or triggers a downstream action,
a council of specialized agents reviews it. One validates data integrity. One checks business rules.
One evaluates cost and efficiency.
The council is not a bottleneck, it runs in parallel, in milliseconds.
The result is structured confidence: every action has been seen by multiple perspectives
before it executes. You get the speed of automation with the oversight of a team.
One agent proposes. The council supports. The action executes with confidence.