Axial extracts open codes, clusters them axially, and surfaces the qualitative signal your metrics miss.
LLM-as-a-Judge gives you scores. Axial tells you why those scores exist — and where they drift.
Auto-surfaces latent themes from LLM outputs without manual labeling. Grounded theory methodology applied at scale across your trace logs.
Groups related open codes into higher-order category families using embedding similarity and hierarchical clustering. Reveals structure in the noise.
Compares your LLM judge scores against cluster-level consensus to surface systematic drift. Catch grade inflation before it corrupts your eval pipeline.
Point Axial at your Langfuse project or LangSmith run. OAuth or API key — no infrastructure changes required. Axial samples at a configurable rate to keep costs low.
Axial runs iterative open coding over sampled LLM outputs — the same inductive process a qualitative researcher would use, but applied at the scale of thousands of traces per hour.
Open codes are embedded and clustered into axial categories — higher-order themes that reveal structural patterns in how your models respond, fail, or drift over time.
Axial compares LLM-as-a-Judge scores against cluster consensus to surface systematic miscalibration — grade inflation, topic-specific bias, temporal drift — with full audit trails.
Connect in under five minutes. No new infrastructure — Axial reads directly from your existing observability tools.
Also works with: Helicone Braintrust Arize Phoenix W&B Weave