IntelliDecision.ai

User Manual

IntelliDecision.ai User Manual

IntelliDecision.ai is a no-code, enterprise-grade decision intelligence platform designed to help organisations build, evaluate, deploy, and govern predictive models and decision strategies at scale.

AI Technology

Built by Corestrat, IntelliDecision.ai eliminates the traditional complexity of machine learning and statistical modelling by embedding advanced AI, automated feature engineering, model optimisation, and decision logic into a guided, intuitive workflow. The platform enables both data scientists and business users to collaborate on building explainable, auditable, and production-ready decision systems, without writing a single line of code.

2. Who IntelliDecision.ai Is For

IntelliDecision.ai is purpose-built for:

  • Financial Services & Fintech (credit risk, underwriting, fraud, collections)
  • Insurance (risk selection, pricing, claims triage)
  • Retail & E‑commerce (customer scoring, churn, offer optimisation)
  • Logistics & Supply Chain (delay risk, vendor risk, demand forecasting)
  • Enterprises adopting AI-driven decision automation
Who we are

Key user personas include: - Risk & Analytics Teams - Business Analysts - Data Scientists - Product Managers - Compliance & Model Governance Teams - Technology & Platform Teams

3. Key Capabilities

No-code / low-code model development

End-to-end ML lifecycle management

Advanced data preprocessing & feature engineering

Automated and manual model building

Explainability via IV, WoE, SHAP, KS, Gini

API-based deployment

Auto documentation & audit readiness

Platform Capabilities

Enterprise-Grade Platform

Built for scale with comprehensive governance and compliance features

4. Platform Architecture & Workflow

IntelliDecision.ai follows a structured six-stage decision intelligence pipeline:

1

Data Ingestion & Project Setup

2

Data Preparation & Feature Engineering

3

Sampling & Target Definition

4

Model Development & Comparison

5

Model Evaluation & Explainability

6

Decision Design, Simulation & Deployment

Each stage is fully integrated, traceable, and configurable.

5. Data Ingestion & Project Management

Project Creation

  • Create new projects or refine existing ones
  • Maintain multiple versions and assumptions
  • Import projects from different environments
Project Creation
Data Upload

Data Upload Options

  • File-based upload: CSV, Excel, Feather, Parquet, delimited text
  • Database ingestion: Databricks, MySQL, Snowflake
  • SQL-based data extraction

Advanced Dataflow Builder (ADB)

The Advanced Dataflow Builder enables complex data preparation using a visual, drag-and-drop canvas:

Multiple dataset ingestion
Horizontal joins (Inner, Left, Right, Outer)
Vertical joins (stacking datasets)
Aggregations (numerical & categorical)
Custom WHERE conditions with rule grouping
Python code execution via reusable functions
Training and validation dataset creation
Memory management at node level

This allows enterprise-grade data engineering—without external ETL tools.

6. Data Preprocessing & Management

Data Preprocessing

Users can choose between: - Let AI Do It (fully automated preprocessing) - Do It Yourself (manual control)

Core Preprocessing Features

Variable Treatment & Governance

  • Identify sensitive variables (e.g. age, gender)
  • Flag high-missing or low-information variables
  • Support fairness-aware modelling

Missing Value Handling

  • Imputation (mean, median, mode)
  • Missingness indicators
  • Smart AI-driven replacement

Outlier Detection

  • Percentile-based capping
  • IQR-based trimming
  • Custom thresholds

Normalisation & Scaling

  • Min-max scaling
  • Z-score standardisation
  • Robust scaling for outliers

Preprocessing can be fully automated via AI or customised step-by-step for full transparency and control.

7. Feature Engineering & Transformation

Feature Engineering Options

  • Code‑It‑Yourself Python feature creation
  • Variable picker for rapid coding
  • Two-way interaction creation
Feature Engineering Options
Variable Transformation

Variable Transformation

  • Automated (AI-selected best transformations)
  • Manual (individual or batch)
  • Retain original and/or transformed variables

Category Encoding

  • One-Hot Encoding
  • Frequency Encoding
  • Batch or variable-level control

Distribution Analysis

  • Interactive histograms for numerical variables
  • Adjustable binning
  • Categorical frequency visualisations
  • Outlier and percentile insights

Feature engineering tools allow users to create, transform, and encode variables — either manually or with AI assistance — ensuring models are built on the most predictive inputs.

8. Sampling & Target Definition

Target Variable Selection

  • Auto-identification of candidate target variables
  • Define positive vs negative outcome categories
Target Variable Selection
Stratified Sampling

Stratified Sampling

  • Default 70/30 train-test split
  • Customisable ratios
  • Ensures target distribution stability

Proper sampling and target definition are critical for building accurate, unbiased predictive models.

9. Variable Insights, Binning & Selection

Information Value (IV) Analysis

  • Fine & final classing
  • Correlation assessment
  • Inferred relationship detection
  • Manual & IV-optimal binning (numeric & categorical)

Clustering (VarClus)

  • Cluster variables using WoE or original values
  • Variance retention or cluster count control
  • Automatic representative variable selection

Multicollinearity Management

  • Correlation matrix
  • VIF-based variable classification
  • Automated correlated variable pruning

Variable Lineage View

  • Full trace of variables added, removed, transformed
  • End-to-end transparency
Variable Insights

10. Model Development & AutoML

Supported Model Types

Decision Trees

Logistic Regression

Random Forest

XGBoost

Neural Networks

Model Development

Model Settings

  • Global parameters (node size, depth, IV, VIF)
  • Score scaling (Base Score, PDO, Odds)
  • Algorithm-specific hyperparameters

Auto Grow Trees

  • Automated tree growth
  • Manual split insertion
  • Node collapse & override
  • IV-guided split recommendations

AutoML (Model.ai)

  • One-click model training
  • Bayesian hyperparameter optimisation
  • Model-specific explainability

11. Model Comparison & Ensembling

Build up to 3 models

Compare using KS, Gini, AUC, F1

Traffic-light performance indicators

Model ensembling (averaging / weighted)

Final model selection

Model Comparison

Compare multiple models side-by-side and create powerful ensembles for optimal performance.

12. Model Evaluation & Explainability

Performance Metrics

  • KS & Gini (Train vs Test)
  • ROC & AUC
  • Confusion matrix
  • Precision, Recall, F1-Score

Explainability Tools

  • SHAP values
  • Variable importance rankings
  • Decision path visualisation
  • Business-friendly reports
Model Evaluation

13. Decision Design, Simulation & Deployment

Decision Strategy Builder

  • Rule-based decision logic
  • Score-to-action mapping
  • Multi-stage approval workflows
  • Champion-challenger testing
Decision Strategy Builder
Deployment Options

Deployment Options

  • REST API endpoints
  • Batch scoring
  • Real-time scoring
  • Version control and rollback

Design, simulate, and deploy decision strategies with full control and monitoring capabilities.

14. Auto Documentation & Governance

Automated Documentation

  • Complete model development audit trail
  • Data lineage tracking
  • Model cards and metadata
  • Regulatory-ready reports

Governance & Compliance

  • Role-based access control
  • Approval workflows
  • Model risk management
  • Compliance with SR 11-7, BASEL, IFRS 9
Documentation & Governance

Enterprise-Ready Platform

IntelliDecision.ai provides comprehensive governance, documentation, and compliance features to meet the strictest enterprise and regulatory requirements.