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
Manual coding took 40% of dev time. With AI handling code generation and security scans, engineering productivity rose 20%.
Strict regulations slowed development. Engineers using AI cut task times from 10 hours to 6 and reduced defects by 30%.
A bug sat for years because the fix meant a month of digging. AI traced the fragmented code to draft a solution in three days.
Manual coding took 40% of dev time. With AI handling code generation and security scans, engineering productivity rose 20%.
Strict regulations slowed development. Engineers using AI cut task times from 10 hours to 6 and reduced defects by 30%.
Analysts couldn't touch the data—every question funneled through engineers, taking hours or weeks. Now they get answers in seconds.
Finance spent six hours stitching four data sources into fully burdened cost models. Now, they use AI to generate them in minutes.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
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
Manual coding took 40% of dev time. With AI handling code generation and security scans, engineering productivity rose 20%.
Strict regulations slowed development. Engineers using AI cut task times from 10 hours to 6 and reduced defects by 30%.
A bug sat for years because the fix meant a month of digging. AI traced the fragmented code to draft a solution in three days.
Manual coding took 40% of dev time. With AI handling code generation and security scans, engineering productivity rose 20%.
Strict regulations slowed development. Engineers using AI cut task times from 10 hours to 6 and reduced defects by 30%.
Analysts couldn't touch the data—every question funneled through engineers, taking hours or weeks. Now they get answers in seconds.
Finance spent six hours stitching four data sources into fully burdened cost models. Now, they use AI to generate them in minutes.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.