The right AI model for the right task — before the bill tells you otherwise.
Sawa benchmarks AI models against your actual tasks and shows you what to use where. Teams scaling AI don't have a model problem — they have a "which model, for what, at what cost" problem. Sawa answers it with measurements.
Cost vs. task performance
Illustrative example — not real benchmark data. Actual results depend on your tasks.
Illustrative: two frontier-class models score within a point of each other at very different costs.
Model choice is being made without data
Token prices move, models multiply, and most teams pick by habit or hype.
The most expensive model is running tasks a model at a fraction of the cost handles just as well.
Nobody owns the "AI bill vs. AI value" question — until finance asks it.
Measurement, mapping, routing, re-checks
Task-level benchmarks
Test models against your real tasks — product descriptions, classification, extraction, support replies — not generic leaderboards.
Cost-performance mapping
See performance and token cost side by side, per task. The tradeoff becomes a decision, not a guess.
Routing recommendations
Sawa tells you which model to assign to which task, and what that saves monthly.
Continuous re-checks
When models or prices change, your benchmarks re-run. Yesterday's right answer gets re-verified.
Measured in production, not in demos
Sawa grew out of our AI enablement work with e-commerce brands running AI in daily operations. Inside our 8-week program, we map where a business spends AI tokens, benchmark the alternatives on their own tasks, and configure the routing. The pattern repeated enough times that we made it a product. E-commerce teams are our first focus — the method works anywhere AI touches the P&L.
E-commerce and digital teams using AI across content, support, and operations — and any team whose AI spend has become a line item worth managing.
Contact ALTREX team
Tell us how your team uses AI today. We'll reply within 2 business days.