Manual coding and testing delayed reward campaigns. Now, AI automates repetitive tasks so 240 engineers focus on core architecture.
One of the largest engagement ecosystems in Brazil connects more than 58 million members to partners, retailers, airlines, and financial institutions through a digital platform maintained by a 240-person engineering team.
Manual effort and productivity variability during the software development and testing phases created severe bottlenecks. These delays slowed down...
“Our approach with Livelo was not about introducing a tool, but about driving measurable business value. The result was not only productivity gains but also a scalable AI adoption framework embedded into Livelo’s engineering culture.”
Rewards and loyalty platform for points accumulation and redemption.
Helped Livelo identify, test, and scale Amazon Q Developer and Kiro for engineering productivity.
Cloud computing platform and on-demand infrastructure services.
Livelo's Software development is part of this use case:
Related implementations across industries and use cases
Every update ripples across Brazil's capital markets. 3,000 AI agents now code and test — engineers review before anything ships.
Each Angular upgrade consumed months of manual testing. Agentic AI finds and fixes breaking changes, freeing engineers for clinical work.
Building prototypes took months. Engineers now use structured markdown to guide an AI IDE in generating standardized code and test cases.
Every update ripples across Brazil's capital markets. 3,000 AI agents now code and test — engineers review before anything ships.
Each Angular upgrade consumed months of manual testing. Agentic AI finds and fixes breaking changes, freeing engineers for clinical work.
Quarterly compliance updates meant costly reshoots. Now scripts regenerate instantly — no cameras, no vendors, no delays.
Most AI tools addressed one step—code review or a single feature—and couldn't run in-house. Factory covers the full SDLC, on-premise.
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.
Manual coding and testing delayed reward campaigns. Now, AI automates repetitive tasks so 240 engineers focus on core architecture.
One of the largest engagement ecosystems in Brazil connects more than 58 million members to partners, retailers, airlines, and financial institutions through a digital platform maintained by a 240-person engineering team.
Manual effort and productivity variability during the software development and testing phases created severe bottlenecks. These delays slowed down...
“Our approach with Livelo was not about introducing a tool, but about driving measurable business value. The result was not only productivity gains but also a scalable AI adoption framework embedded into Livelo’s engineering culture.”
Rewards and loyalty platform for points accumulation and redemption.
Helped Livelo identify, test, and scale Amazon Q Developer and Kiro for engineering productivity.
Cloud computing platform and on-demand infrastructure services.
Livelo's Software development is part of this use case:
Related implementations across industries and use cases
Every update ripples across Brazil's capital markets. 3,000 AI agents now code and test — engineers review before anything ships.
Each Angular upgrade consumed months of manual testing. Agentic AI finds and fixes breaking changes, freeing engineers for clinical work.
Building prototypes took months. Engineers now use structured markdown to guide an AI IDE in generating standardized code and test cases.
Every update ripples across Brazil's capital markets. 3,000 AI agents now code and test — engineers review before anything ships.
Each Angular upgrade consumed months of manual testing. Agentic AI finds and fixes breaking changes, freeing engineers for clinical work.
Quarterly compliance updates meant costly reshoots. Now scripts regenerate instantly — no cameras, no vendors, no delays.
Most AI tools addressed one step—code review or a single feature—and couldn't run in-house. Factory covers the full SDLC, on-premise.
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.