Three live demos showing the kind of analytics, dashboards, and AI quality instrumentation Proteus delivers. All data is simulated — the thinking behind it is real.
Interactive — explore each dashboard below
What you'd see after an Analytics Implementation engagement — a living quality monitor for your LLM in production.
Real-time visibility into eval pass rates, hallucination categories, and response latency across a 90-day window. Built in Amplitude, replicated here as an interactive demo.
Eval Pass Rate — 90 Days
Failure Category Breakdown
Response Latency Distribution (ms)
Proteus Finding
Factual grounding failures spike on Tuesdays — correlating with a weekly data pipeline refresh. The model is being evaluated before the knowledge base has propagated. Fix: delay eval runs by 4 hours post-refresh. Estimated pass rate improvement: +3.1 pp.
What a Health Check engagement surfaces — where your funnel breaks, how severe each drop-off is, and what the data says to fix first.
Funnel visualization with severity-coded drop-off rates and a 6-week retention cohort heatmap. This is what product teams get when they can finally trust their numbers.
Conversion Funnel
Weekly Retention Cohorts
Proteus Finding
The "Create Profile" step has a 43% drop-off — 3× higher than industry median for this funnel type. Tracking analysis shows the event fires inconsistently on iOS Safari. This is a measurement bug, not a UX bug. Fixing the event before shipping a redesign saves ~6 weeks of misdirected engineering.
A live model comparison tool — the kind of structured eval framework Proteus builds as part of an AI Quality Audit.
Side-by-side quality scoring across five dimensions for three LLMs evaluated against the same prompt suite. Select a model to highlight it in the radar chart.
Proteus Finding
Select a model to see the finding.
Everything above reflects real methodology. Book a free 30-minute call and walk away with a concrete sense of what your data is missing.
Or reach out directly: hello@useproteus.io