Each new agent took 8 weeks to ship reliably. A factory framework cut that to days, freeing people for the relationship work AI can't do.
A ride-hailing platform serving passengers and driver partners in Brazil, with AI already embedded in operations, including autonomous vehicle research, before generative AI became widely available.
Each new AI agent required approximately eight weeks to develop reliably enough for public deployment, slowing expansion across customer support,...
“Today, 70% of the support requests and tickets we receive from our users or even our partners are being resolved through intelligent solutions.”
Provider of ride-hailing, food delivery, and smart mobility solutions.
DiDi Global's Operations automation is part of this use case:
Related implementations across industries and use cases
Crash archives spanning decades searched by hand; service reviews took 35 minutes. Engineers now query in seconds; AI handles reviews in 5.
Every new support agent meant months of engineering handoffs. Now the people closest to customers build and ship their own.
Thousands of software modules slowed vehicle development. Now, engineers collaborate with 64 AI agents to write and debug code.
Crash archives spanning decades searched by hand; service reviews took 35 minutes. Engineers now query in seconds; AI handles reviews in 5.
Employees previously broke work into rigid tasks. Now, they delegate high-level goals to agents that work autonomously for hours.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Equipment faults left technicians hunting for technical data while machines sat idle. A chatbot now puts the answer in their hands.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Each new agent took 8 weeks to ship reliably. A factory framework cut that to days, freeing people for the relationship work AI can't do.
A ride-hailing platform serving passengers and driver partners in Brazil, with AI already embedded in operations, including autonomous vehicle research, before generative AI became widely available.
Each new AI agent required approximately eight weeks to develop reliably enough for public deployment, slowing expansion across customer support,...
“Today, 70% of the support requests and tickets we receive from our users or even our partners are being resolved through intelligent solutions.”
Provider of ride-hailing, food delivery, and smart mobility solutions.
DiDi Global's Operations automation is part of this use case:
Related implementations across industries and use cases
Crash archives spanning decades searched by hand; service reviews took 35 minutes. Engineers now query in seconds; AI handles reviews in 5.
Every new support agent meant months of engineering handoffs. Now the people closest to customers build and ship their own.
Thousands of software modules slowed vehicle development. Now, engineers collaborate with 64 AI agents to write and debug code.
Crash archives spanning decades searched by hand; service reviews took 35 minutes. Engineers now query in seconds; AI handles reviews in 5.
Employees previously broke work into rigid tasks. Now, they delegate high-level goals to agents that work autonomously for hours.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Equipment faults left technicians hunting for technical data while machines sat idle. A chatbot now puts the answer in their hands.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.