Weiser AI - Data Quality Framework

YAML-Based Configuration

Define your data quality checks with simple, human-readable YAML. No complex code required - just describe what you want to validate and Weiser handles the rest.

LLM-Friendly Design

Designed for the AI era. Large Language Models can easily understand and generate Weiser configurations, making it perfect for AI-assisted data quality management and automated check generation.

Enterprise-Ready Scale

From startup analytics to enterprise data warehouses. Supports PostgreSQL, Databricks, Snowflake, Cube, and scales to handle millions of records with advanced statistical analysis and anomaly detection.

Simple YAML Configuration

Define data quality checks with intuitive YAML syntax. Perfect for version control, team collaboration, and AI-assisted generation.

# weiser-config.yaml
checks:
  - name: orders_exist
    dataset: orders
    type: row_count
    condition: gt
    threshold: 0
    
  - name: revenue_validation
    dataset: orders
    type: sum
    measure: order_amount
    condition: ge
    threshold: 10000
    filter: status = 'completed'
    
  - name: data_completeness
    dataset: customers
    type: not_empty_pct
    dimensions: [email, phone]
    condition: le
    threshold: 0.05  # Max 5% NULL

LLM-Friendly Design

Weiser's human-readable configuration makes it perfect for AI assistance. LLMs can easily understand, generate, and modify data quality checks.

🤖 AI Code Generation

LLMs can generate Weiser configs from natural language descriptions

📝 Self-Documenting

YAML structure is inherently readable by both humans and AI

🔄 Easy Modification

AI assistants can update and refine existing configurations

💡 Smart Suggestions

LLMs can recommend new checks based on your data schema