Data Engineering Agents

The Dmatch Agent is an AI-powered data quality
engine that automatically validates, cleans, and
harmonizes data across multiple sources (databases, JSON/XML) using GenAI-powered corrections and natural language rule processing, reducing manual effort while ensuring compliance and accuracy.

This DataViz is an AI-powered SQL data
visualization agent that empowers Business
Analysts, Data Scientists, Developers, and Enterprise Architects to generate and visualize complex, cross-database insights without any SQL or Python expertise. Supporting BigQuery, Snowflake, and PostgreSQL, it leverages FAISS-powered schema understanding and Azure OpenAI LLMs for highly accurate, real-time SQL generation with syntax validation, intelligent error correction, and error-driven self-healing.

DFast is a suite of data migration capabilities that
helps clients migrate from various legacy SQL
warehouses to Snowflake, BigQuery, or Databricks. DFast supports migrations from SAP HANA, Teradata, SAP BW, Netezza, MySQL, and more. This agent specifically focuses on SAP Hana to Snowflake migration with a comprehensive feature set.

The Terminal Processor Agent is a cutting-edge
solution that leverages Agentic AI Agents and
AI intelligence to process terminal operations data, extract key insights, and optimize workflows. This agent enables real-time monitoring and analysis of terminal activities, helping operators make informed decisions and improve overall efficiency.

An intelligent data migration agent that automates
the secure and accurate transfer of data between
Salesforce and Veeva systems. It dynamically transforms data structures and injects business logic during runtime, ensuring compatibility even when schema definitions differ. The agent leverages Veeva control tables to orchestrate complex migrations while maintaining full auditability.

The SAS2Python Agent is an AI-powered tool
that automates the conversion of legacy SAS
code into modern, readable Python scripts. It leverages generative AI and data engineering frameworks to translate SAS procedures, macros, and logic into Python using libraries like Pandas and NumPy. This enables faster, cost-effective code migration while supporting integration with modern data platforms. Ideal for enterprises modernizing their analytics infrastructure.