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
Analysts manually reviewed mountains of data for every alert. Now, AI agents summarize and verify facts, boosting efficiency by ~20%.
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
Staff scoured dispersed docs to answer technical queries. An AI now scans internal data to provide answers in under a minute.
Regulatory docs the size of phone books slowed model cycles to months. An AI agent now codes features and writes documentation in hours.
Fragile data pipelines bottlenecked engineers. Now, built-in workflows let teams ship internal AI tools without managing infrastructure.
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.
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
Analysts manually reviewed mountains of data for every alert. Now, AI agents summarize and verify facts, boosting efficiency by ~20%.
Rigid ML tools bottlenecked non-engineers coding with AI. Now, standardized rails and an automated AI debugger let anyone ship safely.
Staff scoured dispersed docs to answer technical queries. An AI now scans internal data to provide answers in under a minute.
Regulatory docs the size of phone books slowed model cycles to months. An AI agent now codes features and writes documentation in hours.
Fragile data pipelines bottlenecked engineers. Now, built-in workflows let teams ship internal AI tools without managing infrastructure.
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.