AI Firewall and guardrails for LLM-based Elixir applications

Interactive Phoenix LiveView demonstrations of the Crucible Framework - showcasing ensemble voting, request hedging, statistical analysis, and more with mock LLMs

Explainable AI (XAI) tools for the Crucible framework

Experimental research framework for running AI benchmarks at scale

Model evaluation harness for standardized benchmarking—comprehensive metrics (F1, BLEU, ROUGE, METEOR, BERTScore, pass@k), statistical analysis (confidence intervals, effect size, bootstrap CI, ANOVA), multi-model comparison, and report generation. Research-grade evaluation for LLM and ML experiments.

Request hedging for tail latency reduction in distributed systems

Intermediate Representation for the Crucible ML reliability ecosystem

Statistical testing and analysis framework for AI research

Data validation and quality library for ML pipelines in Elixir

Metrics aggregation and alerting for ML experiments—multi-backend export (Prometheus, InfluxDB, Datadog, OpenTelemetry), advanced aggregations (percentiles, histograms, moving averages), threshold-based alerting with anomaly detection (z-score, IQR), and time-series storage. Research-grade observability for the NSAI ecosystem.

Advanced telemetry collection and analysis for AI research

Multi-model ensemble voting strategies for LLM reliability

Dataset management and caching for AI research benchmarks

Fairness and bias detection library for Elixir AI/ML systems

CrucibleFramework: A scientific platform for LLM reliability research on the BEAM

Adversarial testing and robustness evaluation for the Crucible framework

Phoenix LiveView dashboard for the Crucible ML reliability stack

Structured causal reasoning chain logging for LLM transparency

Dataset management library for ML experiments—loaders for SciFact, FEVER, GSM8K, HumanEval, MMLU, TruthfulQA, HellaSwag; git-like versioning with lineage tracking; transformation pipelines; quality validation with schema checks and duplicate detection; GenStage streaming for large datasets. Built for reproducible AI research.