The fabric of information space.
Evals for your AI stack, without generating output tokens. 23× cheaper than the typical LLM-as-a-judge — more accurate, lower latency, deterministic.†
† vs Claude Opus 4.7, the only judge that matches our quality — 195× cheaper, 19× faster. qed-bench →
No trade-off.
- 23× cheaper than the typical LLM-as-a-judge195× vs Opus 4.7
- +18% more accurate than the average LLM-as-a-judge
- 19× faster~200 ms a call
- σ = 0 deterministicsame input, same score
The fabric architecture
Eval criteria prompt · rubric · examples
U⊨22A8
Encoders
Semantic Store
DLM
online · low-latency · highly scalable
LLM-as-a-judge retired
Evals Braintrust · LangSmith · Langfuse · DeepEval
Seriously — talk to the MLE.
Name’s Taras. He built this, and he’s interested in scaling your evals. No sign-up, no sales call — just the person who wrote the code, one message away.
FAQ
How do you evaluate without an LLM?
DLM (Discriminative Language Model) runs on an encoder-only architecture and drops a generative layer. Put simply, reads meaning straight from text and scores it in one pass.
Isn’t this just embedding similarity?
No. Cosine similarity tells you how close two texts are, not whether one is good. It has no notion of the criterion you’re scoring, like faithfulness, relevance, tone. A DLM learns that criterion.
When should I still use an LLM?
When you need the judge to argue — a written rationale, a paragraph defending the verdict. U/=22A8 gives you a score and its confidence, not prose; for that explanation, an LLM is still the right tool.
Huh?
Fair — it’s a strange idea the first time you meet it. The MLE is notoriously happy to explain it in more detail than you probably want. Talk to the MLE and ask anything.