Abstract Retail purchases can supplement clinical surveillance because changes in demand for some over-the-counter (OTC) products may appear before confirmed case reports. This article describes an early-stage concept for analysing aggregated GrabMart transaction data to identify unusual changes in ...

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 limitations: Limited property in...

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 nearby driver now, or hol...

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 Merchant Review Summ...

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: Hubble for metadata an...

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 order volume was down...

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 surface tests. When we...

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 a threshold enabling...

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 construction and maint...

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 Context Protocol (MC...

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 full storage-compute ...

Introduction Counter Service is used across Grab’s anti-fraud platform to answer time-windowed count questions, such as recent ride requests by a user or failed payment attempts on a card. The service handles tens of thousands of queries per second (QPS) with about a billion requests per day, while ...

Introduction In Part 1, we introduced Palana, Grab’s Kubernetes-native secure execution platform for autonomous AI agents. We discussed the underlying need for isolated environments and covered its core design principles: treating isolation as the unit of trust, keeping credentials out of agent hand...

Abstract Artificial intelligence (AI) agents are moving from experiments into everyday engineering workflows. They can read code, call application programming interfaces (APIs), run tests, create merge requests, answer Slack messages, and keep long-running state. That makes them useful, but it also ...

Introduction Grab is migrating from heavy base images like Ubuntu to Distroless images to reduce security risks. By stripping containers down to the bare application and its runtime, we eliminate unnecessary binaries and Common Vulnerabilities and Exposures (CVEs). This migration is more than a comp...

Introduction: The journey of documentation at Grab In early 2021, Grab adopted a Docs-as-Code approach to address gaps in our technical documentation processes, as illustrated in our blog post Embracing a Docs-as-Code. Inspired by the practices of other market leaders, we integrated documentation in...

Introduction Data drives every decision we make at Grab. As our operations scale, so does our need for robust, real-time data ingestion and processing frameworks. Enter Hugo: our self-service data platform that has long empowered teams to seamlessly route data into our Data Lake. Today, Hugo is evol...

Introduction Long integrated development environment (IDE) sync/indexing times can quietly erode developer productivity, making code navigation sluggish, spiking memory usage, and slowing down Jetpack Compose preview updates, turning the IDE into a bottleneck rather than a helpful tool. For Android ...

Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratization of intelligence, ensuring that anyone making a business call has immediate access to trustworthy answers. Over the last two years, model capability has crossed a threshold enabling...

Introduction Ensuring the reliability of Apache Flink deployments in Grab is crucial for the availability of our business-critical, real-time applications. While all applications are tested in a staging environment before getting promoted to the production environment, there is still a class of issu...

Introduction In Part I, we discussed why Grab is investing in a data mesh, referred to as the Signals Marketplace within Grab, as part of our evolving data culture. We also explained how data certification aids teams in reliably reusing data across different domains. However, cultural change doesn’t...

Introduction In Part I, we discussed why Grab is investing in a data mesh, referred to as the Signals Marketplace within Grab, as part of our evolving data culture. We also explained how data certification aids teams in reliably reusing data across different domains. However, cultural change doesn’t...

Introduction In our recent AutoTrack SDK blog post, we shared how we solved the challenge of capturing complete user journeys across our mobile app. One of the most promising applications we highlighted was automating iOS UI (User Interface) test case generation using the rich interaction data to au...

Introduction In our recent AutoTrack SDK blog post, we shared how we solved the challenge of capturing complete user journeys across our mobile app. One of the most promising applications we highlighted was automating iOS UI (User Interface) test case generation using the rich interaction data to au...

Abstract Grab’s Analytics Data Warehouse (ADW) team supports over 1,000 users each month and manages an extensive repository of more than 15,000 tables, which powers approximately 50% of all queries within our data lake. However, the manual process of addressing “quick questions” is time-consuming a...

Grab is Southeast Asia’s leading superapp, providing a suite of services that bring essential needs to users throughout the region. Its offerings include ride-hailing, food delivery, parcel delivery, mobile payments, and more. With safety, efficiency, and user-centered design at heart, Grab remains ...

Introduction In a previous post, we discussed Project Bonsai, our initiative to reduce the Grab app’s download size. We successfully reduced the Android Application Package (APK) download size by 26%. This reduction offers a substantial advantage: it minimizes download friction, allowing users to do...

Adoption overview The illustration below encapsulates how Cursor is scaled across Grab, achieving rapid and widespread adoption that accelerated software development and empowered non-technical teams to build solutions. Figure 1: Adoption overview of AI tool Cursor in Grab. Multi-tool strategy Grab ...

Introduction At Grab, we’ve been exploring ways to dramatically reduce container startup times for our data platforms. Large container images for services like Airflow and Spark Connect were taking minutes to download, causing slow cold starts and poor auto-scaling performance. This blog post shares...

Abstract You’ve vibe-coded an AI assistant that’s a game-changer for your team. It works perfectly on your laptop. But when you try to deploy it company-wide, everything falls apart. This is what is known as “deployment slop”—the messy reality when quick AI prototypes hit the enterprise world. Your ...