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Transform raw data into production-ready scorecards with absolute reproducibility and rigorous stability.

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Automated Interactive Development Framework

AIDflow is a pipeline-first framework for binary classification scorecards in regulated environments. Built for industries where model governance, temporal stability, and regulatory transparency are non-negotiable. Framework where a human directs and an AI agent executes the full pipeline — stability-first, audit-ready, deployable. No notebook spaghetti. No 6-week committee cycles. Built over 15 years of credit scoring, fintech R&D, and regulatory practice. AIDflow Preview

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Who This Is For

  • AI Orchestration Endpoints — one command surface that lets LLM agents drive the full pipeline (genesis → training → champion) and return a deployable scoring model without human intervention.
  • Data Scientists building scorecards in Fintech, Banking, or Insurance who need full control over every transformation step.
  • Model Risk Teams who require complete audit trails, reproducible execution, and standardized reporting artifacts.
  • ML Engineers who need a production scoring artifact with zero framework dependency at inference time.
If your scoring model must survive a committee review, AIDflow is your development environment.

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Why AIDflow?

  • Human-in-the-Loop. Every phase is a decision point. Pause, inspect, re-configure, re-run — without losing state. All in an interactive UI.
  • Context-Driven. A single ProjectContext links every stage from ingestion to champion export. No fragile variable chains.
  • Stability-First + Ensemble. Temporal screening runs before training. Four orthogonal agents compete on stabilized data. An objective scoring system promotes the champion.
  • Full Feature Lineage. Trace any data/feature from raw origin to final score. Every intermediate state is registered and navigable by stage.
  • Modular Extensibility. Add a new fitter in minutes. It auto-propagates to the UI, panels, and reporting — no manual wiring.
  • Self-Contained Scoring. The exported artifact encapsulates all encoding and model state. Inference requires no training data or prior pipeline state.
  • Audit-Ready Governance. Every production artifact is packaged with full lineage, SHAP explainability vectors, and performance benchmarks for seamless regulatory approval.

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Get Started in Seconds

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Install & Run AIDflow

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The Golden Path to Production

AIDflow guides you through a standardized five-phase journey:
  1. Data Phase: Ingest → Clean → Stabilize
  2. Encoding Phase: Numerical → Categorical → Interaction → WOE Encoding
  3. Training Phase: Train an ensemble of 4 specialized ML Agents
  4. Selection Phase: Predict → Compare → Transition → Promote Champion
  5. Inference Phase: Prediction Tests → Production Export
Read the Developer Handbook