Deciphering legacy systems bottlenecked senior reviewers. Now, early-career devs use Claude to navigate code, freeing seniors to build.
Italy's leading independent payment network, with 6 million consumers and more than 450,000 merchants, running on a mature Java and Spring codebase that processes every payment on the network.
The engineering team skewed toward earlier-career engineers who spent more time orienting in unfamiliar legacy services than writing code, leaving...
“A junior engineer facing an unfamiliar service needs help understanding the system, not help finishing a line of code.”
Mobile payment app and independent network for retail and peer-to-peer payments.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Satispay's Code modernization is part of this use case:
Related implementations across industries and use cases
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Strict security blocked CLI coding tools. Using a pre-configured AI assistant, engineers compress weeks of manual migration into days.
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Strict security blocked CLI coding tools. Using a pre-configured AI assistant, engineers compress weeks of manual migration into days.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Some member headshots looked genuinely unusable. Runway transformed each into broadcast-ready stills—no cross-country studio shoot required.
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.
Deciphering legacy systems bottlenecked senior reviewers. Now, early-career devs use Claude to navigate code, freeing seniors to build.
Italy's leading independent payment network, with 6 million consumers and more than 450,000 merchants, running on a mature Java and Spring codebase that processes every payment on the network.
The engineering team skewed toward earlier-career engineers who spent more time orienting in unfamiliar legacy services than writing code, leaving...
“A junior engineer facing an unfamiliar service needs help understanding the system, not help finishing a line of code.”
Mobile payment app and independent network for retail and peer-to-peer payments.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Satispay's Code modernization is part of this use case:
Related implementations across industries and use cases
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Strict security blocked CLI coding tools. Using a pre-configured AI assistant, engineers compress weeks of manual migration into days.
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Strict security blocked CLI coding tools. Using a pre-configured AI assistant, engineers compress weeks of manual migration into days.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Some member headshots looked genuinely unusable. Runway transformed each into broadcast-ready stills—no cross-country studio shoot required.
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.