Grab
3 stories filed under Grab on Beyond Market Intelligence. The newest of them: “Grab Cuts Spreadsheet-Style Data Delays With a Smarter Storage Shift”, “From Two Weeks to One Hour: Grab's Framework Simplifies AI Agent Deployment”, and “How Grab Uses AI Agents to Automate Analytics and Empower Teams”. Grab cut spreadsheet-style data delays by rethinking how it stores high-volume Counter Service data. Two weeks to one hour. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Grab story on Beyond Market Intelligence, newest first.
Grab Cuts Spreadsheet-Style Data Delays With a Smarter Storage Shift
Grab cut spreadsheet-style data delays by rethinking how it stores high-volume Counter Service data. Moving from a wide column database to Aerospike, with storage abstraction, shadow traffic, and gradual migration, slashed production p99 read latency by roughly 50%. Disk usage dropped to 1 TB from 3 TB, and per-node costs fell up to 50%. That is a smarter storage shift worth exploring. For deeper engineering lessons, our piece on building Python AI libraries in production pairs well with this story.
From Two Weeks to One Hour: Grab's Framework Simplifies AI Agent Deployment
Two weeks to one hour. That's the compression Grab achieved by standardizing its AI agent deployment with LLM-Kit, a framework now powering over 500 internal services. This isn't just about speed; it's about centralizing the messy parts of production, from secret handling to runtime tool discovery, so teams can focus on outcomes. We think that's a practical, human-centered approach to scaling intelligence. For a deeper look at how AI understands structure, explore our piece on how LLMs navigate token space. It's a natural next step.
How Grab Uses AI Agents to Automate Analytics and Empower Teams
Grab's analytics team cut mechanical work from 44% to 30% in just four months, and the lesson is clear: AI agents thrive when you pair autonomy with human oversight. Their approach, combining certified data and context management, lets self-service analytics handle metric and SQL requests that once demanded analyst time. That's not just efficiency; it's a smarter division of labor. If you're questioning how much to trust these systems, our piece on verifying AI's understanding offers a practical starting point.