GrabGPT: How Grab built an internal ChatGPT-like tool after a failed support chatbot experiment
Grab's ML Platform team support channels were overwhelmed with repetitive user inquiries, with on-call engineers spending more time answering questions than building innovative solutions.
An initial documentation-fed chatbot built on chatbot-ui and GPT-3.5-turbo failed because the model's token context window could not accommodate the full platform documentation, requiring heavy summarization that only covered a handful of FAQs; embedding search also proved ineffective.
GrabGPT became one of the most widely used internal tools at Grab, with 300 users registering on day one, over 3,000 users and 600 daily active users by month three, and eventually almost all Grabbers using it.
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Frequently asked questions
What did this team achieve with this AI workflow?
GrabGPT became one of the most widely used internal tools at Grab, with 300 users registering on day one, over 3,000 users and 600 daily active users by month three, and eventually almost all Grabbers using it.
What tools did this team use?
chatbot-ui, GPT-3.5-turbo, catwalk, GrabGPT, OpenAI, Claude, Gemini.
What results were reported?
Users registered day 1: 300; New users day 2: 600; New users week 1: 900; Total users month 3: over 3000 (source-reported, not independently verified).
What failed first in this deployment?
An initial documentation-fed chatbot built on chatbot-ui and GPT-3.5-turbo failed because the model's token context window could not accommodate the full platform documentation, requiring heavy summarization that only…
How is this it support AI workflow structured?
Support channels overwhelmed → Documentation chatbot attempt → Token-limit and embedding failure → GrabGPT built and deployed → Rapid company-wide adoption.