LIVE AutoLineage v0.3.0 on PyPI 288 hooks installed MIT licensed Austin, TX
An AI Reliability Company

Make your AI systems reliable, safe, and audit-ready. RudriQ builds the observability layer every production AI system will need.

We're starting with data lineage — and shipping toward a unified platform that covers data, models, LLMs, and regulatory compliance under one import.

Current product AutoLineage — pip install autolineage Frameworks pandas · scikit-learn · PySpark Next up LLMGuard · RudriDash · ComplianceEngine
Install AutoLineage → View source
§ I
288+
Hooks installed
§ II
3
Frameworks tracked
§ III
85µs
Per-op cost (negligible at scale)
§ IV
1
Line to install
§ 01 · The Problem

AI fails silently. We make it legible.

Every layer of the AI stack has failure modes that only surface in production — and every existing tool covers exactly one of them.

01
?

Your model's F1 dropped overnight.

Was it a data change, a feature regression, or a preprocessing bug? Without end-to-end lineage, you're guessing. Most teams add print statements and pray.

02
?

Your LLM just hallucinated in production.

Which prompt? Which model? How much did it cost? Was it a prompt injection? Existing LLM tools show you spend — not safety. We fix both.

03
?

EU AI Act compliance is due in 90 days.

You need a model card, a data provenance log, audit trails, and a risk assessment. Right now that's six tools and a consultant. It should be one report.

STREAMING · autolineage.trace SESSION #0042 · 0x7f3a
rudriq@pipeline:~$ python fraud_model.py
§ 03 · Shipping Today

Meet AutoLineage.

Our first product. Zero-code data lineage for Python ML pipelines. Live on PyPI since April 2026.

● Live · v0.3.0 on PyPI

AutoLineage

Zero-code data lineage. One import. Full coverage.

  • 288 hooks across pandas, sklearn, PySpark Every transform, fit, predict, metric — automatic.
  • Anomaly detection + root-cause localization Programmatically find bugs before users do.
  • SHA-256 content hashing + lineage fingerprints Cryptographic data integrity across runs.
  • Plugin architecture — extensible in ~200 LoC Add any Python ML library without touching core.
  • 85µs per hooked operation, negligible at production scale Microbenchmarked: 95% CI [78, 91] µs across 15 trials.
Install from PyPI → Read the source
pipeline.py
import autolineage.auto # done. import pandas as pd from sklearn.ensemble import RandomForestClassifier df = pd.read_csv("data.csv").dropna() X, y = df.drop(columns=['y']), df['y'] model = RandomForestClassifier().fit(X, y) # Every operation above: tracked. # Lineage, metrics, anomalies — all captured.
§ 04 · Vision

AutoLineage is just the beginning.

RudriQ's thesis: every production AI system needs observability across four domains. We've shipped one. We're building the next three as standalone products under the RudriQ suite.

Product · 2026

LLMGuard

Reliability monitoring for LLM-powered applications.

  • OpenAI, Anthropic, LiteLLM SDK hooks
  • On-device hallucination detection
  • Prompt injection blocking
  • Token cost tracking per call
  • Latency budgets with alerting
Product · 2027

RudriDash

Unified dashboard for the entire AI stack.

  • Interactive lineage DAG visualization
  • Real-time alerting + Slack integration
  • Team workspaces with RBAC
  • Drift detection across environments
  • Enterprise SSO and audit logs
Product · 2027

ComplianceEngine

Regulatory documentation on demand.

  • EU AI Act auto-documentation
  • HIPAA audit trails
  • OCC model risk reports
  • One-command model cards
  • Audit-ready by default
§ 05 · Why RudriQ

Every tool covers one slice. We're building the whole stack.

Today's ML observability market is fragmented. Teams duct-tape five products together and still miss the thing that breaks. RudriQ's bet: unified platform, one import, shipped as a suite.

Capability MLflow Evidently Arize Langsmith WhyLabs RudriQ
Data pipeline lineageAutomatic, operation-level — — — — — ●
Model training + metricsFit, predict, evaluate hooked ● partial ● — ● ●
LLM reliabilityHallucinations, cost, safety — partial ● ● ● ●
Zero code changesJust import, nothing else — — — — — ●
End-to-end single graphData → model → LLM → report — — — — — ●
Compliance reportsEU AI Act, HIPAA, OCC — — partial — — ●

Every AI team will need this infrastructure.
We're the ones building it.

RudriQ is early, but AutoLineage is real and ready today. Install it, star it, break it — and come build with us.

Start with AutoLineage → Star on GitHub Partner with us
RudriQ · An AI Reliability Company · Austin, 2026