Privacy rules bottlenecked AI scaling. A secure internal platform now cuts AML investigation time by days and security resolution by 50%.
A leading Canadian fintech serving over 2 million users with spending, savings, and credit-building solutions built entirely on cloud infrastructure.
Strict financial data privacy standards made it difficult to scale generative AI across operations without compromising compliance. Isolated tools...
“Our goal was to provide KOHO employees with better, easier, and more secure access to large language models.”
Digital banking platform for spending, saving, and credit building in Canada.
Cloud computing platform and on-demand infrastructure services.
KOHO's Internal AI platform is part of this use case:
Related implementations across industries and use cases
Rent, phone bills, utilities paid on time—proof scattered across silos. Consolidated into AI profiles, the invisible became creditworthy.
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
Trade data was buried in messy chat logs. GenAI now mines the text for pricing, turning raw noise into new revenue streams.
Fragile data pipelines bottlenecked engineers. Now, built-in workflows let teams ship internal AI tools without managing infrastructure.
Clients waited days for manual API provisioning. Now, their enterprise AI agents directly query 400M business records instantly.
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.
Privacy rules bottlenecked AI scaling. A secure internal platform now cuts AML investigation time by days and security resolution by 50%.
A leading Canadian fintech serving over 2 million users with spending, savings, and credit-building solutions built entirely on cloud infrastructure.
Strict financial data privacy standards made it difficult to scale generative AI across operations without compromising compliance. Isolated tools...
“Our goal was to provide KOHO employees with better, easier, and more secure access to large language models.”
Digital banking platform for spending, saving, and credit building in Canada.
Cloud computing platform and on-demand infrastructure services.
KOHO's Internal AI platform is part of this use case:
Related implementations across industries and use cases
Rent, phone bills, utilities paid on time—proof scattered across silos. Consolidated into AI profiles, the invisible became creditworthy.
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
Trade data was buried in messy chat logs. GenAI now mines the text for pricing, turning raw noise into new revenue streams.
Fragile data pipelines bottlenecked engineers. Now, built-in workflows let teams ship internal AI tools without managing infrastructure.
Clients waited days for manual API provisioning. Now, their enterprise AI agents directly query 400M business records instantly.
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.