Optimized intelligence per token, second, dollar.
how it works
Eval. Train. Run.
- 1 /Eval. We design evaluations that best understands your real work
- 2 /Train. We train an open model for maximized intelligence output
- 3 /Run. We serve and route requests across models efficiently
case 1
Case 1 · Bithumb (digital asset exchange, Korea)
We first built Bithumb's AI research reports on a GPT-5.6 Sol multi-stage workflow. Then we post-trained an open model to write the same reports in one call with higher accuracy
| GPT-5.6 Sol workflow → trained model | change |
|---|---|
| cost | -96.5% |
| tokens | -67.3% |
| generation calls per report | about 11 → 1 |
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