Indicium Launches AI Data Squads for Databricks Migrations

Leader in data and AI services, Indicium, recently announced the launch of AI Data Squads as a Service —a delivery model designed to assist organizations in accelerating complex migrations, modernizing platforms, enhancing data quality, and laying the foundation for operational intelligence.

Human expertise combined with agentic AI: the winning combination for data quality

The new Squads as a Service will be built on Indicium’s IndiMesh framework and powered by Agentic AI. The new service will offer speed and quality assurance, enabling enterprises to unlock real value from their data.

“AI can accelerate delivery, but the real value comes from combining automation with hands-on expertise and platform depth,” said Daniel Avancini, chief data officer at Indicium. “Our teams bring together years of experience with Databricks and dbt, enhanced by agents that streamline the work without cutting corners. That is how customers modernize faster and build with confidence.”

The AI Data Squads, powered by IndiMesh and enabled by agentic AI technology, are purpose-built to address significant challenges faced by organizations, including poor data quality, fragmented platforms, and manual processes that can slow delivery and limit impact.

According to Indicium, each Squad is a hybrid team combining certified engineers and consultants with embedded AI agents for expertise across Databricks, dbt Cloud, and the modern data stack. These teams offer platform fluency, structured delivery, and practical insight to every engagement and can adapt to each organization. 

Agentic AI tools address a variety of needs, from architecture to governance

AI Data Squads as a Service is an all-in-one delivery model designed to help enterprise teams modernize their platforms, enhance data quality, and unlock the full potential of their data investments. This is enabled through each Squad being equipped with a suite of purpose-built AI agents from IndiMesh process maps and trained on years of experience with the modern data stack.

The list of agents includes:

• Prompt2Pipeline Agent: These agents automate translation of PySpark, pandas, and SSIS projects into modular dbt models for Databricks with aligned best practices and MCP validation.
• Architecture Agents: Makes recommendations for implementation patterns based on over 250 proven reference architectures. 
• Data Pipeline Agents: For streamlining ETL and ELT development, performance optimization, and workflow reliability.
• Governance Agents: Meant to ensure compliance through automated validation checks mapped to internal policy and industry standards.
• MLOps Agents: These agents automate deployment, monitoring, and versioning for machine learning models across production standards.
• Documentation Agents: For maintaining live documentation from code and configuration, including lineage and data dictionaries.
• Analytics Agents: Enables intuitive query generation and quick insights using natural language exploration.
• Project Management Agents: Meant for tracking progress, allocating resources, and flagging delivery risks using inputs from IndiMesh’s competency matrix.
• Quality Assurance Agents: For automating rigorous testing of data flows, code, and outputs to ensure all components meet internal and regulatory standards.

All of these Agents are deployed securely in customer environments, according to Indicium, and no data is transferred externally. IT teams can access agents through developer tools and APIs to enhance velocity, reduce manual effort, and strengthen governance throughout the delivery process.

Databricks has been at the forefront of meeting the rising demand for data in building and deploying AI solutions. Read more about their recent partnership with SAP for Databricks Data Intelligence Platform integration.