One evaluation framework.Any LLM output.
The deterministic alternative to LLM-as-judge. Six scoring categories. Per-claim audit trail. The same methodology grades RAG responses, agent outputs, copilot drafts, and anything else your LLM produces — same input, same score, every run.
Your eval says everything is fine.
These scores look great. The reality is different.
Confident responses. Fabricated claims.
The response reads perfectly. But look closer.
Example shown: healthcare RAG copilot. The same hallucination pattern shows up in agent outputs, sales drafts, and any LLM-generated content.
The treatment protocol recommends starting with metformin at 500mg twice daily. Clinical trials showed a 47% improvement in outcomes when combined with lifestyle interventions.
Patients should be monitored every 3 months for the first year. The landmark 2019 study by Dr. Chen et al. confirmed these findings across 12,000 patients.
Six categories. Deterministic scoring. Same answer, every run.
49 dimensions across six categories cover the full surface of what can go wrong with an LLM output. Each score reproduces byte-for-byte on the same input.
Quality
Correctness, completeness, relevance to the request. Did the response actually answer the question?
Safety
Toxicity, bias, PII leakage, prompt-injection resistance. Catch unsafe outputs before they reach users.
Grounding
Claim-by-claim verification against source material. NLI + numeric checks. Hallucination rate per response.
Semantic
Embedding-based similarity to reference answers. Topic drift, off-domain detection, semantic faithfulness.
Coherence
Multi-turn consistency, context retention, contradiction detection across an interaction sequence.
Advanced
Reasoning depth, citation quality, structured-output validity, edge-case behaviors. The metrics that catch what generic scoring misses.
One framework. Many applications.
The same scoring methodology grades whatever your LLM produces — the response text, the synthesized answer, the inline draft.
Retrieval-augmented generation
Claim-level grounding against retrieved chunks. Citation verification. Hallucination catch rates 6× cleaner than RAGAS on public benchmarks.
Multi-step agent workflows
Score each agent step and the handoffs between them — not just the final answer. Deterministic checks across the whole trace: tool correctness (right tool, right arguments), cross-step coherence (do the agents contradict each other), aggregation grounding (is the final answer actually supported by the upstream steps), and loop detection. When a multi-agent workflow goes wrong, you see exactly which step introduced it.
Inline assistants & copilots
Score inline drafts, completions, and suggestions across all six categories. Compare suggestion variants on quality, safety, and groundedness before rolling out company-wide.
Stop guessing. Start scoring.
No credit card. See your first deterministic evaluation in under 10 minutes.