Company engineering blog
Grab's Engineering team solves critical transportation challenges and makes transport freedom a reality for 620 million people in Southeast Asia.
10 posts in the last 90 days · newest 6 Oct
Introduction Existing systems estimate user price sensitivity primarily from spending behavior or demographic proxies. They do not systematically account for residential property values, which can indicate a user’s financial circumstances. This omission creates three…
The short story Consider a Friday evening. A food order arrives from a mall in the city center. One driver is nearby; another is finishing a drop-off and will be available shortly; a second order from the same mall may or may not appear in the next two minutes. Dispatch the…
Introduction Merchant reviews contain useful details about food quality, portion size, packaging, and value. Finding these details often requires reading many comments. Aggregate ratings simplify comparisons, but they do not explain what shaped each score. We built the Automated…
Introduction In the first two parts of this series, we described how Grab approaches data mesh through the Signals Marketplace: a way for teams to publish, discover, and reuse trusted data products across domains. Part II introduced the foundational tools behind certification…
Introduction The first Jarvis Pro prototype could produce answers that sounded right. That was the problem. One early answer looked polished: it named the merchant, summarized the week, and recommended pushing promotions before the next review. It was also wrong. The merchant’s…
Introduction What worried us wasn’t the hallucination, it was the subtle plausibility. Answers an engineer could easily read past and accept: a right-looking Structured Query Language (SQL) query, a plausible tool call, an innocent profile update, or a patch that satisfied the…
Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratisation of intelligence, ensuring that anyone making a business call has immediate access to trustworthy answers. Over the last two years, model capability has crossed…
Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, products, and merchants changes rapidly. Current methods for KG…
Part 1: From one support bot to a framework At Grab, AI agents have evolved from interesting team prototypes into production services used every day by millions of merchants, drivers, and consumers. Today, more than 500 services run on our internal agent framework, over 50 Model…
Introduction: The evolution of Grab’s Data Lake At Grab’s scale, managing petabytes of data across billions of S3 objects demands more than a storage layer. It demands a robust architectural primitive that supports the high-concurrency needs of a modern “Lakehouse.” Our goal is…
