How AI is transforming analytics at Grab
As AI agents take on more of the analytics loop, Grab's analysts are moving from producing artefacts to owning questions, judgement, and decisions. This post introduces the five-level autonomy ladder behind that shift, the platform capabilities that make each level possible, and what it looks like in production today.
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Articles
How AI is transforming analytics at Grab
Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions
Agent platform (Part 1): How we help Grab build and run AI agents at scale
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption
Migrating Counter Service storage: Design choices and learnings
Scaling out Distroless adoption With AI
Palana (Part 2): Architecting isolation, identity, and auditability for AI agents
Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents
From decentralized Docs-as-Code to a centralized repository: Evolving Grab's documentation strategy
The Hugo evolution: Engineering Grab's unified, one-click data ingestion platform with Apache Flink
Scaling developer experience: How we improved Android Studio in a large monorepo
Enhancing Flink deployment with shadow testing
Data Mesh at Grab (Part II): The foundational tools behind certification
From firefighting to building: How AI agents restored our team’s core productivity
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