# aribaiq

**aribaiq** is an open-source Python library for **AI-powered procurement, sourcing, and supplier intelligence analytics**.

It is designed to help teams turn raw sourcing or ERP-exported data into practical insights through:

- supplier intelligence scoring
- spend visibility and optimization signals
- ESG-aware evaluation
- responsible AI checks
- AI-ready prompt generation for copilots and LLM workflows

The package is lightweight, vendor-neutral, and suitable for analytics teams, engineers, procurement strategists, and open-source developers building modern source-to-pay intelligence solutions.

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## Why aribaiq?

Procurement teams often have large datasets but limited decision visibility.

Common challenges include:

- inconsistent supplier performance evaluation
- difficulty identifying concentration risk
- scattered ESG and compliance indicators
- limited explainability in AI-assisted sourcing decisions
- manual effort in turning analytics into executive summaries

**aribaiq** helps solve these problems with transparent analytics, governance-aware checks, and AI-ready outputs.

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## Core Features

### Supplier intelligence scoring
Ranks suppliers using business-relevant signals such as:
- on-time rate
- defect rate
- ESG score
- spend concentration

### Spend analytics
Highlights:
- category concentration
- supplier spread
- consolidation opportunities
- single-supplier exposure

### ESG scoring
Provides weighted ESG evaluation support for suppliers.

### Responsible AI checks
Includes:
- missingness checks
- invalid value detection
- representation imbalance warnings

### GenAI-ready prompts
Builds provider-agnostic prompts for:
- internal copilots
- chat assistants
- LLM-based reporting pipelines

### CLI included
Run analytics directly on CSV, JSON, or JSONL files.

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## Installation

```bash
pip install aribaiq


## Author
Jagadeesh Vasanthada

## LICENSE

MIT License

Copyright (c) 2026

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