Every session, sellers and analysts started from zero—explaining Stripe to AI that had never heard of it. Kai already knows the company.
A global payments and financial infrastructure platform used by millions of businesses, with a dedicated AI platform team responsible for all AI tooling across the company.
When Claude Code launched in late 2024, demand for AI agents surged across the company, but non-engineers faced significant barriers: terminal...
“The Deep Agents layer solves all the non-Stripey problems, so that we can focus on solving the Stripey agent problems. You can take a bunch of middleware off the shelf. The composable skill patterns, the composite backend, it provides the right structure, but still enough flexibility.”
Financial infrastructure platform for online payments and business operations.
Framework and developer platform for building LLM-powered applications.
Stripe's Knowledge assistant is part of this use case:
Related implementations across industries and use cases
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
AI was restricted to tech teams. By embedding governance, 20k staff now build agents that cut legal reviews from 4 hours to 1.
Legacy models crashed at 1M tokens. Bankers now use a single AI agent to process 100M+ token deals and build custom skills without code.
One AE held the team's institutional knowledge—90% of his day in Slack. A digital double in his voice freed him to sell.
Teams manually searched siloed databases. Now, custom connectors link HubSpot and Notion to Claude, saving sales dozens of hours weekly.
Minor compliance updates once forced full video reshoots. Now, course creators generate and edit AI avatar videos directly from text.
Shifting to B2B sales exposed disconnected workflows and anecdotal forecasts. Now, AI analyzes interactions to surface deal intelligence.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Admin tasks bogged down 364k consultants. Copilot now drives 40M+ actions, reclaiming $150M in time for client advisory.
Every session, sellers and analysts started from zero—explaining Stripe to AI that had never heard of it. Kai already knows the company.
A global payments and financial infrastructure platform used by millions of businesses, with a dedicated AI platform team responsible for all AI tooling across the company.
When Claude Code launched in late 2024, demand for AI agents surged across the company, but non-engineers faced significant barriers: terminal...
“The Deep Agents layer solves all the non-Stripey problems, so that we can focus on solving the Stripey agent problems. You can take a bunch of middleware off the shelf. The composable skill patterns, the composite backend, it provides the right structure, but still enough flexibility.”
Financial infrastructure platform for online payments and business operations.
Framework and developer platform for building LLM-powered applications.
Stripe's Knowledge assistant is part of this use case:
Related implementations across industries and use cases
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
AI was restricted to tech teams. By embedding governance, 20k staff now build agents that cut legal reviews from 4 hours to 1.
Legacy models crashed at 1M tokens. Bankers now use a single AI agent to process 100M+ token deals and build custom skills without code.
One AE held the team's institutional knowledge—90% of his day in Slack. A digital double in his voice freed him to sell.
Teams manually searched siloed databases. Now, custom connectors link HubSpot and Notion to Claude, saving sales dozens of hours weekly.
Minor compliance updates once forced full video reshoots. Now, course creators generate and edit AI avatar videos directly from text.
Shifting to B2B sales exposed disconnected workflows and anecdotal forecasts. Now, AI analyzes interactions to surface deal intelligence.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Admin tasks bogged down 364k consultants. Copilot now drives 40M+ actions, reclaiming $150M in time for client advisory.