AI Reliability Platform

Data · Models · LLMs · Compliance

Make your AI systems reliable, safe, and audit-ready

Rudriq monitors your entire AI stack — from raw data through model training to LLM responses — so you catch failures before your customers do.

Get early access GitHub

Compliance EU AI Act · HIPAA · OCC · Model Cards · audit trails Building

LLM layer Hallucination detection · prompt injection · cost tracking · latency Building

Model layer scikit-learn · XGBoost · LightGBM — fit, predict, evaluate tracked Live

Data layer pandas · PySpark · Polars — transforms, lineage, drift, quality Live

// the platform

Four layers. One import. Full coverage.

Most tools cover one layer. Rudriq covers the entire AI lifecycle — and connects every layer into one unified trace.

Data quality + lineage

Live

Track every data transformation automatically. Know exactly what happened to your data between file read and model input.

Automatic lineage tracking across pandas, PySpark, Polars
Shape changes, column diffs, row counts per operation
SHA-256 data integrity verification
Data drift detection and schema change alerts
Powered by AutoLineage (open source, on PyPI)

Model monitoring

Live

Track model training, predictions, and evaluation automatically. Know when accuracy decays before customers notice.

scikit-learn fit/predict/transform/score hooks
Hyperparameter capture for every training run
train_test_split tracking with sizes and random state
All sklearn metrics (accuracy, F1, RMSE, R²) logged automatically
XGBoost and LightGBM support (plugin architecture)

LLM safety + monitoring

Building

Monitor LLM API calls, detect hallucinations, block prompt injection — all without external AI dependencies.

OpenAI + Anthropic + LiteLLM SDK hooks
Hallucination detection (local embeddings, no API calls)
Prompt injection detection via pattern matching
Token cost tracking per model, per call, per day
Latency monitoring with threshold alerts

Compliance + audit

Planned

Auto-generate the documentation regulators require. EU AI Act, HIPAA, OCC — covered from day one of your pipeline.

EU AI Act high-risk system documentation
HIPAA-aligned audit trails for healthcare AI
OCC model risk management for financial services
Auto-generated Model Cards and Datasheets
One-command compliance reports

// the differentiator

End-to-end trace. Nobody else does this.

When an AI system produces a wrong answer, Rudriq tells you whether the data was wrong or the model was wrong — in one trace.

Data origin: customer_data.csv (SHA: a3f2c8…)
  → dropna(CustomerID)     [50,000 → 46,800 rows]
  → filter(status=active)   [46,800 → 34,400 rows]
  → assign(+3 features)    [34,400 × 12 cols]
  → StandardScaler.fit_transform
  → train_test_split      [train=27,520 test=6,880]
  → RandomForest.fit      [100 trees, depth=10, 2.4s]
  → predict               [6,880 predictions]

Metrics: RMSE=0.42 · R²=0.94 · MAE=0.31
Duration: 3.8s total pipeline

Coverage: raw data → transforms → model → output

LangsmithLLM only

EvidentlyDrift + LLM

MLflowExperiments

RudriqFull chain

// how it works

From import to insight

No config files. No proxy servers. No SDK wrappers to learn.

Import

One line: import rudriq. All hooks install at import time.

Track

Every pandas, sklearn, PySpark operation recorded. Data + model + metrics in one DAG.

Trace

End-to-end lineage: raw data → transforms → training → prediction → evaluation.

Report

Compliance docs, audit trails, and lineage graphs generated on demand.

// why rudriq

What others miss

Every competitor covers one or two layers. Rudriq covers the full chain.

Capability	Langsmith	Evidently	Arize	MLflow	WhyLabs	Rudriq
Data pipeline lineage	✕	✕	✕	✕	✕	✓
ML model tracking	✕	✓	✓	✓	✓	✓
LLM monitoring	✓	✓	✓	✕	✓	✓
Zero code changes	✕	✕	✕	✕	✕	✓
End-to-end trace	✕	✕	✕	✕	✕	✓
Multi-framework	✕	✕	✕	✕	✕	✓
Open source	✕	✓	✕	✓	✕	✓