Overview
Three tools, three interfaces
Inputs, settings and reports for each Python tool. Files stay in your browser; calculations remain with the Python programs.
● ● ● dataqmonitor
dataqmonitor
Check CSV data for missing values, outdated records and rule violations
Example preview · synthetic data
completeness91.7 %attention
freshness100 %ok
availability75 %attention
consistency83.3 %attention
CSV + fetch log → config.json → Report 2.0
Open tool →
● ● ● llm-eval
llm-eval
Compare LLM configurations on a defined set of test cases
Example preview · synthetic data
matrix_complete27/27pass
case_acceptance0.89pass
boundary_cases3/3pass
latency p952.4 spass
benchmark.json + run.json → Report 1.0
Open tool →
● ● ● resqmonitor
resqmonitor
Monitor prediction accuracy, costs and response times
Example preview · synthetic data
accuracy78.0 %attention
outcome_coverage94 %ok
baseline_accuracy−12 ppattention
case_mix8 %ok
CSV + baseline → config.json → Report 2.0
Open tool →
Source code