Delivery and mobility / Ride-hailing and delivery
Grab: A shared AI service for employees
- Company
- Grab
- Country
- Singapore
- Adoption stage
- In operation
- Source published
- Date basis
- The date the source was published. It can differ from the date adoption started.
- How the source was checked
- Read the full source text
The work problem
Grab's machine learning platform team was buried in user questions. Questions piled into a Slack channel and the on-call engineer spent time answering the same ones. The engineer first tried building a chatbot that reads the platform documentation and answers. But GPT-3.5-turbo handled 8,000 tokens at a time, so documents of more than 20,000 words had to be cut below 800 words, and that approach was hard to scale.
Technology and data
The engineer changed direction and decided to build an internal ChatGPT. He extended the open framework chatbot-ui and added Google sign-in. He connected it to catwalk, the in-house model serving platform, built it over a weekend and released it internally. GrabGPT runs over a private route so company data does not leave. It is not tied to one vendor's model and uses models from OpenAI, Claude, Gemini and others. Every conversation is kept as an audit record that the data security and governance teams can review.
Results
300 people signed up on the first day, 600 on the second, and another 900 during the first week. By the third month users passed 3,000 and daily active users were 600. The author wrote that nearly every employee now uses GrabGPT. Employees in regions where ChatGPT is not available also gained access to the same tool.
Limits and open questions
The user numbers are figures given by the author. The source carries no numbers showing hours saved or a change in company-wide productivity.
Sources
- From failure to success: The birth of GrabGPT, Grab’s internal ChatGPTengineering.grab.com, Accessed
Compiled from public sources. These are not results from ATF Works customers.