# Grab
> Southeast Asia's leading everyday "super-app" that provides ride-hailing, food delivery, grocery shopping, and digital financial services.

## Articles

### [From deployment slop to production reality: How BriX bridges the gap with enterprise-grade AI infrastructure](https://yomu.fyi/post/from-deployment-slop-to-production-reality-how-brix-bridges-the-gap-wi.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sneh Agrawal
- Published: Jan 16, 2026

Internal AI prototypes frequently fail enterprise rollouts due to diverging versions, security oversights, hardcoded credentials, and infrastructure bottlenecks. BriX addresses this deployment gap by turning AI rollout into a configuration-driven platform rather than an engineering rewrite. Built on a synchronous streaming architecture, it routes user prompts through a React frontend using Server-Sent Events, a FastAPI gateway, and LangGraph orchestration. The platform integrates model switching, centralized prompt locks, and standardized Model Context Protocols for governed enterprise data access.


### [Demystifying user journeys: Revolutionizing troubleshooting with auto tracking](https://yomu.fyi/post/demystifying-user-journeys-revolutionizing-troubleshooting-with-auto-t.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Alex Prokofiev
- Published: Dec 23, 2025

Tracking user journeys across hundreds of mobile UI components via manual clickstream instrumentation frequently caused data gaps and prolonged issue resolution. Grab designed AutoTrack, an SDK that automatically records application lifecycle states, launch triggers, user interactions, and screen contexts. The system monitors transitions across foreground, background, and inactive states alongside entry points like deep links and push notifications. On the client side, AutoTrack captures complete screen hierarchy metadata from native Android activities, fragments, and iOS view controllers, redacting sensitive data to comply with privacy regulations. This automated trace enables engineering teams to reproduce elusive bugs, route incidents to domain owners, and generate automated UI test cases from real-world usage patterns.


### [How Grab is accelerating growth with real-time personalization using Customer Data Platform scenarios](https://yomu.fyi/post/how-grab-is-accelerating-growth-with-real-time-personalization-using-c.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Saubhagya Awaneesh
- Published: Dec 18, 2025

Grab previously relied on daily batch attribute updates in its Customer Data Platform, which created engineering bottlenecks and hindered time-sensitive engagement opportunities. To address this limitation, the team introduced Scenarios, a self-serve real-time personalization capability embedded within the platform. The architecture processes real-time event triggers from Grab's Scribe platform using Apache Flink, enriches incoming events with historical context from StarRocks, and evaluates pre-trained machine learning classifiers. Computed outputs sync to Kafka streams or Amphawa, an internal feature store powered by AWS DynamoDB, maintaining end-to-end latencies under fifteen seconds. Across more than a dozen production deployments, including real-time interventions for subscription abandonment within fifteen minutes, the platform achieved over a 3% conversion uplift compared to batch campaigns.


### [A Decade of Defense: Celebrating Grab's 10th Year Bug Bounty Program](https://yomu.fyi/post/a-decade-of-defense-celebrating-grab-s-10th-year-bug-bounty-program.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Pei Shan Yap
- Published: Dec 1, 2025

Grab's bug bounty program has operated for a decade in partnership with HackerOne, expanding from an initial cohort of 23 researchers to over 850 active participants across global regions. The program's scope broadened between 2023 and 2024 to encompass artificial intelligence systems, Indonesian financial services, and a dedicated bounty table for mobile-specific security issues. Grab extended external testing coverage through live hacking appearances at ThreatCon 2023 and DEFCON 32, as well as invite-only anniversary campaigns with regional clubs in Germany, Morocco, and India. Internal cybersecurity teams manage vulnerability reports by emphasizing rapid triage times, direct communication, and payouts upon triage. Over the decade, reported vulnerabilities transitioned from foundational flaws toward more sophisticated and emerging threat categories.


### [Real-time data quality monitoring: Kafka stream contracts with syntactic and semantic test](https://yomu.fyi/post/real-time-data-quality-monitoring-kafka-stream-contracts-with-syntacti.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Yuanzhe Liu
- Published: Nov 26, 2025

Kafka streams often suffer from syntactic and semantic data quality issues that propagate undetected to downstream consumers without real-time validation. Grab addressed this challenge by developing a standardized contract testing and observability framework within its Coban platform. Stakeholders define schema rules and field-level semantic validations, which can be recommended using large language models and anonymized sample data. A transformation engine converts these contracts into inverse SQL queries executed continuously by a FlinkSQL Test Runner on a dedicated consumer group. Problematic records are published to an alert topic, archived to AWS S3, and surfaced via Slack notifications and UI field-highlighting across more than 100 critical Kafka topics.


### [SpellVault’s evolution: Beyond LLM apps, towards the agentic future](https://yomu.fyi/post/spellvault-s-evolution-beyond-llm-apps-towards-the-agentic-future.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Felix Haryanto Lie
- Published: Nov 21, 2025

Grab developed SpellVault as an internal no-code platform to democratize the creation of AI applications backed by Retrieval-Augmented Generation (RAG) and plugin integrations. To advance beyond static retrieval and linear input-output processing, the platform transitioned from its legacy executor to a graph-based execution model supporting branching, looping, and ReAct agent patterns. Capabilities like Python code execution and internal repository searching were unbundled from the prompt builder and consolidated alongside user plugins into unified Native and Community Built Tools. The platform also introduced a drag-and-drop deterministic workflow designer, automated task scheduling, and support for the Model Context Protocol (MCP).


### [Grab's Mac Cloud Exit supercharges macOS CI/CD](https://yomu.fyi/post/grab-s-mac-cloud-exit-supercharges-macos-ci-cd.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Madushan Gamage
- Published: Nov 6, 2025

Scaling iOS CI/CD workloads across multiple large mobile applications led to substantial expenses and queue delays under rented cloud macOS infrastructure. To mitigate network latency to Git servers and cut compute overhead, Grab relocated its build fleet from a US cloud provider to a colocation data center in Malaysia. The infrastructure deployment relies on over 200 bare-metal Mac minis across 42RU racks, purposefully avoiding virtualization solutions that introduced performance and stability penalties. Phased migration and guardrail monitoring verified cluster stability while achieving 20% to 40% reductions in pipeline and app build times. Operating the regional bare-metal cluster over an estimated three-year hardware replacement lifecycle is projected to yield 2.4 million USD in total cost of ownership savings.


### [How we built a custom vision LLM to improve document processing at Grab](https://yomu.fyi/post/how-we-built-a-custom-vision-llm-to-improve-document-processing-at-gra.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jia Chen
- Published: Nov 4, 2025

Document processing for identity verification across Southeast Asia presents challenges due to varied layouts and non-Latin scripts. Traditional OCR and off-the-shelf vision models struggle with accuracy, high latency, or lack of regional language training data. Grab addressed this by creating synthetic regional datasets, using an automated labeling pipeline named Documint, and evaluating open-source multimodal architectures. After initial LoRA fine-tuning failed on complex scripts like Thai and Vietnamese, full-parameter fine-tuning of Qwen2-VL 2B yielded substantial gains. To optimize deployment costs and latency, the team constructed a custom 1B parameter model pairing a Qwen2-VL vision encoder with a Qwen2.5 0.5B language decoder, achieving performance within 3 percentage points of the 2B model at significantly lower latency.


### [Machine-learning predictive autoscaling for Flink](https://yomu.fyi/post/machine-learning-predictive-autoscaling-for-flink.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Minh Nhat Nguyen
- Published: Oct 30, 2025

Managing resource allocations for expanding Apache Flink stream-processing workloads often leads to over-provisioning or unstable reactive scaling. Reactive autoscaling with Kubernetes Horizontal Pod Autoscaling triggers severe restart spikes because pipelines must reprocess backlogged records from checkpoints, inducing feedback loops and scaling fluctuations. To prevent these spirals, a predictive vertical autoscaling system uses upstream Kafka message throughput as an independent workload metric. The architecture forecasts future workload using time-series models and maps the projected throughput to required TaskManager CPU allocations through a trained regression model. A custom controller vertically scales TaskManager CPU resources before traffic changes arrive, avoiding restart-induced latency spikes.


### [Modernising Grab’s model serving platform with NVIDIA Triton Inference Server](https://yomu.fyi/post/modernising-grab-s-model-serving-platform-with-nvidia-triton-inference.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Daniel Tai
- Published: Oct 21, 2025

Grab's machine learning serving platform, Catwalk, experienced mounting technical debt, elevated latency, and rising costs from maintaining disparate inference engines for various frameworks. To address these limitations, the team adopted NVIDIA Triton Inference Server to establish a unified engine, starting with an ONNX migration. They built a custom Triton manager component featuring a proxy layer to translate legacy API requests alongside a server manager that handles model downloads, verification, configuration, and health checks. Within ten days, over half of online deployments migrated seamlessly without requiring client code modifications. The transition reduced p90 latency from 120ms to 20ms on large transformer models and cut average infrastructure spend by approximately 20 percent across evaluated services.


### [Highly concurrent in-memory counter in GoLang](https://yomu.fyi/post/highly-concurrent-in-memory-counter-in-golang.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Naveen Kumar Jakuva Premkumar
- Published: Oct 6, 2025

High database CPU utilization caused by relentless write traffic for marketing campaign counts prompted Grab to build an in-memory aggregation buffer rather than migrate from SQL to NoSQL. The team implemented an in-memory counter in Go that batches usage updates and periodically flushes them to persistent storage. To eliminate the serialization bottlenecks of mutex-locked maps under high concurrency, the design adopted Go's sync.Map using atomic CompareAndSwap retries for value updates alongside LoadAndDelete for periodic background flushes. Because a finite set of campaign keys is repeatedly accessed, operations hit the internal read map fast path nearly 99% of the time, achieving a threefold throughput improvement over standard mutex locks in benchmarks. In production, this architecture decreased database update queries by 68% and reduced master database CPU utilization from 35% to 18%.


### [User foundation models for Grab](https://yomu.fyi/post/user-foundation-models-for-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Abhinav Rai
- Published: Sep 26, 2025

Traditional recommendation systems at Grab relied on siloed, manually engineered features that struggled to capture sequential interaction data and cross-service user intent across its superapp ecosystem. To resolve these limitations, Grab developed a custom user foundation model built on a transformer architecture designed to learn from both tabular profile data and time-series clickstream interactions. The architecture unifies diverse data modalities—including categorical identifiers, numerical values, text, and geographical coordinates—by framing inputs as key-value token pairs. Custom positional embeddings and attention masks ensure the model processes tabular attributes as unordered sets while maintaining the sequential order of time-series events. The pre-trained model generates holistic user embeddings and supports direct fine-tuning across downstream tasks such as fraud detection, churn prediction, and advertisement optimization.


### [Powering Partner Gateway metrics with Apache Pinot](https://yomu.fyi/post/powering-partner-gateway-metrics-with-apache-pinot.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Alvis Chew
- Published: Sep 23, 2025

Grab needed to power real-time analytics dashboards for its Partner Gateway, tracking API status codes and latency across datasets reaching 6.8 billion rows over 30-day windows. Initial aggregation queries on datasets exceeding 150GB frequently timed out past 10 seconds, failing to meet the platform's 300-millisecond service level agreement. To support low-latency Online Analytical Processing queries, Grab routed metric streams through Apache Kafka and Apache Flink into Apache Pinot. Query execution was then accelerated by partitioning Kafka topics by metric name, adding rounded time interval columns, and implementing Star-tree indexes for multidimensional pre-aggregation.


### [Taming the monorepo beast: Our journey to a leaner, faster GitLab repo](https://yomu.fyi/post/taming-the-monorepo-beast-our-journey-to-a-leaner-faster-gitlab-repo.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Nagendra Gangwar
- Published: Sep 16, 2025

Grab's decade-old Go monorepo grew to 12.7 million commits and 250GB of Git data, causing Gitaly replication delays of up to four minutes that routed all read traffic exclusively to the primary node and slowed developer operations. After staging tests proved that shallow history reduced replication lag from hundreds of seconds to under three seconds, standard rewriting tools like git filter-repo and git rebase failed due to complex merge histories and repository scale. To overcome runner memory limits and lengthy git garbage collection cycles, the engineering team implemented a custom two-phase migration script. The script selectively migrated 2,000+ critical dependency tags and one month of recent history, flattening merge commits, embedding legacy hashes for traceability, and reducing total commit volume by 99.9%.


### [Data mesh at Grab part I: Building trust through certification](https://yomu.fyi/post/data-mesh-at-grab-part-i-building-trust-through-certification.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Chun Rong Phang
- Published: Aug 19, 2025

Rapid business growth across multiple verticals led Grab's centralized data engineering model to become an unscalable bottleneck, resulting in duplicate pipelines, ambiguous ownership, and broken downstream dependencies. To resolve these issues, the organization initiated a data mesh journey called Signals Marketplace that decentralizes data management and treats data as a product. A central data certification system establishes formal data contracts covering schemas, SLAs, freshness, and retention, while assigning clear Business Data Owners and Technical Data Owners. Breaches in contract guarantees automatically generate Data Production Incident tickets to enforce accountability and root-cause fixes. Consequently, 75% of internal queries now target certified assets, redundant tables saw a 400% year-over-year deprecation increase, and the total number of top-used datasets dropped by over 58%.


### [The evolution of Grab's machine learning feature store](https://yomu.fyi/post/the-evolution-of-grab-s-machine-learning-feature-store.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Daniel Tai
- Published: Jul 24, 2025

Grab redesigned its initial machine learning feature store, Amphawa, to address high-dimensional data, complex entity retrieval, and versioning challenges during feature updates. The new architecture adopts a feature-table model where data scientists output Parquet datasets to Amazon S3 using Spark, which are then atomically ingested into Amazon Aurora PostgreSQL via a reverse ETL workflow. To prevent noisy-neighbor contention and optimize infrastructure costs, the platform utilizes Aurora's distributed storage to separate reads from writes. Grab pairs Aurora Serverless on writer nodes to scale up during daily batch ingestion with Provisioned instances on read replicas for steady serving traffic.


### [Grab's service mesh evolution: From Consul to Istio](https://yomu.fyi/post/grab-s-service-mesh-evolution-from-consul-to-istio.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Hilman Kurniawan
- Published: Jul 16, 2025

Grab operated over 1,000 microservices across hybrid infrastructure using Consul alongside a fallback mechanism called Catcher. Single-point-of-failure vulnerabilities in Consul servers and limited support for multi-cluster operations prompted an evaluation of alternative mesh technologies, ultimately leading to the selection of Istio. Grab avoided the standard single-control-plane-per-cluster pattern by deploying multiple external control planes in dedicated Kubernetes clusters arranged in active-active pairs. Migration began in Q4 2024, shifting traffic across AWS and GCP while handling both HTTP and gRPC protocols with gradual traffic-shifting and rollback mechanisms.


### [DispatchGym: Grab’s reinforcement learning research framework](https://yomu.fyi/post/dispatchgym-grab-s-reinforcement-learning-research-framework.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Tan Sien Yi
- Published: Jul 7, 2025

Applying reinforcement learning to dispatch systems is often hindered when the chosen control levers exert weak influence over reward functions. To streamline research, Grab built DispatchGym, a framework that connects reinforcement learning algorithms to a dispatch process simulation via the Gymnasium API. The simulation emphasizes directional accuracy over absolute precision, allowing researchers to evaluate relative metric shifts across supply and demand scenarios. Built in modular Python and accelerated with Numba, the system allows data scientists to test code locally and launch distributed Spark executions with a single command-line call. The framework has been used to evaluate various contextual bandit models and action sampling strategies for tuning dispatch hyperparameters.


### [Counter Service: How we rewrote it in Rust](https://yomu.fyi/post/counter-service-how-we-rewrote-it-in-rust.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jia Long Loh
- Published: Jun 20, 2025

The Integrity Data Platform team rewrote Counter Service, a high-throughput Golang microservice serving event counts for fraud rules and machine learning models, to evaluate the operational return on investment of Rust. Rather than performing a line-by-line translation, engineers approached the service as a black box, reimplementing core application logic from scratch to satisfy established gRPC contracts across Scylla and Redis. The team resolved internal Go tooling dependencies by building custom configuration template parsers using the nom parser combinator and selected targeted open-source crates such as fred.rs and Cadence. Adapting to Rust required navigating cooperative, stackless async execution compared to Go's preemptive concurrency model, alongside managing borrow checker constraints. Ultimately, the rewrite achieved a 70% reduction in infrastructure costs while maintaining comparable service performance.


### [The complete stream processing journey on FlinkSQL](https://yomu.fyi/post/the-complete-stream-processing-journey-on-flinksql.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Calvin Tran
- Published: Jun 12, 2025

Grab previously relied on Apache Zeppelin notebooks for interactive stream processing exploration, but faced lagging Flink version upgrades, five-minute cluster cold starts, and poor integration with internal platforms. To address these limitations, the team migrated to a shared FlinkSQL gateway architecture structured into compute, integration, and query layers. The new setup uses a Hive Metastore catalog to expose Kafka topics as relational tables, while a custom control plane handles authentication and headless REST APIs over Flink's native interface. For production workflows, a configuration-based portal accepts SQL logic and automatically provisions and deploys Flink pipelines within ten minutes. This transition reduced ad-hoc query response times to under one minute and eliminated the need to maintain version adapter shims.


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