Key Features
- Feasibility analysis across multiple AI providers
- Token-level cost estimates before you commit
- Model recommendations matched to the task shape
- Side-by-side provider comparison on price and capability
- Clear answers to "can AI do this, and is it worth it"
Built For
Builders deciding whether — and with which model — to throw AI at a problem
Vision
Every AI feature starts with the same gamble: is this even feasible, which model should run it, and what will it cost at scale. Augur turns that gamble into an estimate. Describe the task and it reports feasibility, the best-fit provider, and the token economics — so the decision is grounded in numbers instead of vibes.
The Problem
Teams pick AI models the way they pick lottery numbers: a hunch, a benchmark they half-remember, a default they never revisited. The cost surprises arrive in the bill, and the capability surprises arrive in production. There is no cheap way to ask "can AI do this, and what does it cost" before you build it.
Key Differentiators
- Multi-provider by default: compares across providers instead of assuming one.
- Economics up front: token costs are first-class, not a postmortem line item.
- Task-shaped recommendations: matches the model to the job, not to fashion.
- A real answer: "yes, with this model, at this cost" — not a shrug.