From Legacy to AI-Ready: How MongoDB AMP Accelerates Modernization

About this episode

Shilpa Kolhar discusses how MongoDB's AMP facilitates the transition from legacy systems to AI-ready architectures.

Summary In this episode, Shilpa Kolhar, SVP of Product and Engineering at MongoDB, discusses using MongoDB as a unified foundation for AI-driven and agentic applications. She explains how the Application Modernization Platform (AMP) accelerates the transition from legacy relational systems to a document-first architecture, driven by the need for AI-readiness and speed of change. Shilpa highlights MongoDB's features, such as its native JSON document model, Atlas Vector Search, auto-embeddings, and integrated search, which help eliminate drift and latency across operational data, indexing, and vectors, emphasizing the importance of keeping context, transactions, and embeddings together for real-time AI use cases. She shares best practices for re-architecting legacy systems, including schema validation and versioning patterns to tame schema drift, aggregation pipelines for consistent reads, and pragmatic standardization across services, while also detailing AMP's approach to scoping large estates and the balance of LLM-powered automation with human-in-the-loop governance. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management If you…

People in this episode

Host: Tobias Macey

Guest: Shilpa Kolhar

Topics covered

• AI readiness
• application modernization
• document-first architecture
• legacy systems
• real-time AI use cases

Keywords

• MongoDB
• AMP
• AI-driven applications
• schema validation
• real-time data

Mentioned in this episode

Organizations: MongoDB

Products: Application Modernization Platform (AMP), Atlas Vector Search

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