Engineers capped at basic chat, complex work out of reach. Now 700 tackle cross-jurisdiction compliance reviews in hours, not weeks.
A 221-year-old Swiss private bank managing ~CHF 800 billion for a small number of ultra-high-net-worth clients, built on a deliberate model: deeply personalized service at the front, maximum automation at the back.
While central teams built internal AI tools, engineers and business users were limited to basic chat interfaces and couldn't build anything complex...
“We try to be very high-touch at the front, but as automated as we can at the back, so that we can provide the best service possible. It's a bit different than many banks out there.”
Wealth and asset management, and private banking services.
Trained over 500 Pictet employees through Claude Code workshops and co-organized a hackathon
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Pictet's Software development is part of this use case:
Related implementations across industries and use cases
Colleagues hunted through knowledge systems for answers; Global Markets hand-sorted emails before ops could act. Claude now handles both.
Routine admin bogged down 120,000 staff. Secure custom GPTs now automate workflows, freeing bankers for strategy and risk analysis.
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Nastya wrote specs three times per feature, plus a week of competitive research. Now she writes once—AI handles desktop, web, and mobile.
Quarterly compliance updates meant costly reshoots. Now scripts regenerate instantly — no cameras, no vendors, no delays.
Treated as a plug-in, not a platform. Now 370+ scenarios handle 10M+ daily AI calls — and new models ship in a day, not two weeks.
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.
Engineers capped at basic chat, complex work out of reach. Now 700 tackle cross-jurisdiction compliance reviews in hours, not weeks.
A 221-year-old Swiss private bank managing ~CHF 800 billion for a small number of ultra-high-net-worth clients, built on a deliberate model: deeply personalized service at the front, maximum automation at the back.
While central teams built internal AI tools, engineers and business users were limited to basic chat interfaces and couldn't build anything complex...
“We try to be very high-touch at the front, but as automated as we can at the back, so that we can provide the best service possible. It's a bit different than many banks out there.”
Wealth and asset management, and private banking services.
Trained over 500 Pictet employees through Claude Code workshops and co-organized a hackathon
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Pictet's Software development is part of this use case:
Related implementations across industries and use cases
Colleagues hunted through knowledge systems for answers; Global Markets hand-sorted emails before ops could act. Claude now handles both.
Routine admin bogged down 120,000 staff. Secure custom GPTs now automate workflows, freeing bankers for strategy and risk analysis.
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Legacy tools failed at complex migrations. Claude Code now navigates the full codebase, cutting API builds from 2 days to 5 hours.
Nastya wrote specs three times per feature, plus a week of competitive research. Now she writes once—AI handles desktop, web, and mobile.
Quarterly compliance updates meant costly reshoots. Now scripts regenerate instantly — no cameras, no vendors, no delays.
Treated as a plug-in, not a platform. Now 370+ scenarios handle 10M+ daily AI calls — and new models ship in a day, not two weeks.
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.