DEP.AI

Agent-driven data engineering at scale

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Modern data platforms are growing more complex, and data engineering must keep up with the speed and scale enterprises need.

DEP.AI, Nagarro’s agent-based data engineering accelerator, uses autonomous AI agents to build, operate, and optimize data pipelines across the entire lifecycle - from ingestion and schema management to data quality and transformation.

DEP.AI integrates seamlessly with today’s cloud and data ecosystems. By adapting instantly to change, enforcing quality, and continuously optimizing performance, DEP.AI helps enterprises scale faster, improve performance, control costs, and strengthen trust in their data. It enables consistent reporting, data-driven insights, and a resilient foundation for AI initiatives.

why DEP.AI?

 
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Faster pipeline delivery
 
Speed up pipeline development by 60% using AI agents for ingestion, mapping, and transformation, reducing manual effort.
 

 
Faster onboarding to analytics
 
Up to 50% faster onboarding of data sources and use cases up with ready-to-use, industry-specific data models and reusable templates.
 
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Lower data platform costs
 
Up to 40% cost savings across the data engineering cycle with optimized infrastructure usage, automation, and pipeline efficiency. 
 
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Enterprise ready by design
 
Built-in governance, security, and compliance ensure the data is trusted, auditable, and production-ready from day one.
 
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Scales with business growth
 
Designed to support growing data volumes, domains, and teams without linear increases in engineering effort or complexity.
 

 
Resilient error-free pipelines
 
Agent-driven monitoring and self-healing capabilities minimize failures, ensure consistent data quality, and reduce operational firefighting.

How DEP.AI empowers enterprise-scale transformation

 

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Platform features

Data ingestion & transformation

Adaptive and agentic ecosystem

AI agents that monitor, adapt, and optimize data pipelines in real time, automatically handling failures, changes, and performance issues.

data pipeline automation

Domain-centric data models

Pre-built, industry-aligned data models for BFSI, Manufacturing, and Retail that accelerate analytics readiness and business use case adoption.

Cloud and resource provisioning

Cloud-agnostic, multi-cloud architecture

A single codebase that runs seamlessly across AWS, Azure, GCP, Snowflake, and hybrid environments without vendor lock-in.

data quality

Low-code, accelerated development

Low-code, visual workflows and reusable components enable faster development, testing, and iteration of data pipelines.

data processing

Data mesh enablement

Supports decentralized data ownership and self-serve infrastructure while maintaining consistency through federated governance.

AI integration

AI-driven data governance

Built-in AI-driven lineage, masking, and cataloging ensure data security, compliance, and trust by design.

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