CrucibleFramework: A thin orchestration layer for experiment pipelines. Provides pipeline execution, stage behaviour, and optional persis...

Dataset management and caching for AI research benchmarks

AI Firewall and guardrails for LLM-based Elixir applications. Provides prompt injection detection, data leakage prevention, jailbreak det...

Comprehensive benchmarking framework for AI research. Measures latency, throughput, cost, and reliability with percentile analysis and Nx...

Fairness and bias detection library for Elixir AI/ML systems. Provides comprehensive fairness metrics, bias detection algorithms, and mit...

Multi-model ensemble prediction with voting strategies for AI reliability. Leverages BEAM parallelism for massively concurrent LLM queries.

Request hedging for tail latency reduction in distributed systems. Implements Google's 'Tail at Scale' with adaptive strategies. Reduces ...

Model versioning, artifact storage, and lineage tracking for ML pipelines

Structured causal reasoning chain logging for LLMs. Captures decision-making processes with cryptographic verification for transparency a...

Adversarial testing and robustness framework for AI models with 25 attacks (character/word/semantic perturbations, prompt injection, jail...

Explainable AI (XAI) tools for the Crucible framework. Includes LIME implementations, SHAP-like explanations, feature attribution, and mo...

Advanced telemetry collection and analysis for AI research

Model deployment orchestration with health checking and rollback

Unified ML training infrastructure for Elixir/BEAM

Feedback collection, drift detection, and active learning for ML pipelines