Origin shows how your organization’s context and AI budget move through the AI stack, so you can understand what the investment supports and where it can work harder.
Vendor bills show tokens and seats. They don't show which workflows created the cost, and what you got in return.
Providers often default to their most capable models, even when the work doesn't need them.
The instructions and workflows that make AI useful are spread across providers, making their value difficult to see.
Leadership wants to know what AI is supporting. Most teams still only have the bill.
See what is driving token spend and connect the cost back to the work.
Track usage and estimated cost across prompts, sessions, models, providers, tools, agents, and employees on endpoints.
This total is a lower bound. 78% of attributed tokens are priced. 39.7B tokens are excluded because no model was recorded, or the per-call count was impossible.
Cluster AI activity by team, topic, and workflow, so spend stops being a black box and turns into something you can explain.
Clusters are named from the work itself, so spend arrives already grouped by what it bought.
See where frontier models are the default for routine work, and where the same problem is being solved more than once.
Area is spend. Red is work a cheaper model could do. Hover to isolate a flow.
$3.33K went to routine lookups a cheaper model handles identically.
Answer the questions every AI program will face. What did we spend? Where did it go? What did it produce?
This total is a lower bound. 78% of attributed tokens are priced. 39.7B tokens are excluded because no model was recorded, or the per-call count was impossible.
Origin turned our token telemetry into a savings plan.
It showed what was driving the bill, and found savings without moving important work to weaker models.
Origin connects AI usage to the work behind it, so you can see what the investment supports and where to put the next dollar.