USEFUL INFERENCE™ · IN DEVELOPMENT

Accounting for what your inference actually does.

We're building the attribution layer that separates productive inference from redundant or wasted calls.

The problem

Inference spend has outgrown visibility

Inference has become the largest ongoing AI cost center for most teams — yet most organizations can't easily tell productive inference calls from redundant, retried, or wasted ones.

As test-time compute and multi-step agent calls grow, the gap between total inference spend and useful inference spend keeps widening.

What we're building

Productive vs. wasted inference

Attribution that separates inference calls that produced value from ones that didn't.

Cost-per-outcome metrics

Metrics tied to actual task outcomes, not just token counts or call volume.

Multi-model visibility

One view across providers and models, wherever inference actually runs.

Efficiency reporting

Reports built for teams that need to defend or optimize inference budgets.

Why now

Inference now dominates cost

For most deployed AI products, inference has become the dominant, ongoing cost — not the one-time training run.

Agentic workloads multiply calls

Multi-step agents and test-time compute mean far more inference calls per task than a single chat completion.

Waste hides in aggregate bills

Without attribution, it's nearly impossible to tell useful inference from retries, loops, and dead ends.

Get in touch

Let's talk

We're early and building selectively. If inference cost accountability is a live problem on your team, we'd like to talk.