Kazakhstan's data laws blocked cloud AI. BCC anonymized 10,000+ calls on-premises, then trained multilingual speech models in the cloud.
One of Kazakhstan's largest banks, operating 20 branches and more than 150 outlets serving over 3 million clients, under strict national regulations requiring sensitive financial and customer data to remain within the country.
The bank's on-premises infrastructure couldn't support scalable AI and ML workloads, and provisioning new servers took months at high cost....
“We opted to build a hybrid, multicloud architecture. This approach provided us with flexibility in scaling computational resources as well as the ability to use unique cloud services to address various technological challenges and use cases.”
Full-service commercial bank offering retail and corporate financial solutions.
Cloud computing platform and on-demand infrastructure services.
Bank CenterCredit's Call transcription is part of this use case:
Related implementations across industries and use cases
Agents typed furiously while customers waited on hold. AI now summarizes calls, saving the team 250,000 hours annually.
Analyzing new segments required manually tagging 50K reviews. Now, AI automatically translates and categorizes multilingual feedback.
High call volumes bottlenecked 130+ agents. Now, a secure internal LLM resolves app queries and summarizes chats, even during air raids.
Agents typed furiously while customers waited on hold. AI now summarizes calls, saving the team 250,000 hours annually.
Strict privacy rules prevented sending raw call transcripts to AI. Now, an automated system safely strips and restores sensitive data.
Quarterly compliance updates meant costly reshoots. Now scripts regenerate instantly — no cameras, no vendors, no delays.
Engineers capped at basic chat, complex work out of reach. Now 700 tackle cross-jurisdiction compliance reviews in hours, not 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.
Kazakhstan's data laws blocked cloud AI. BCC anonymized 10,000+ calls on-premises, then trained multilingual speech models in the cloud.
One of Kazakhstan's largest banks, operating 20 branches and more than 150 outlets serving over 3 million clients, under strict national regulations requiring sensitive financial and customer data to remain within the country.
The bank's on-premises infrastructure couldn't support scalable AI and ML workloads, and provisioning new servers took months at high cost....
“We opted to build a hybrid, multicloud architecture. This approach provided us with flexibility in scaling computational resources as well as the ability to use unique cloud services to address various technological challenges and use cases.”
Full-service commercial bank offering retail and corporate financial solutions.
Cloud computing platform and on-demand infrastructure services.
Bank CenterCredit's Call transcription is part of this use case:
Related implementations across industries and use cases
Agents typed furiously while customers waited on hold. AI now summarizes calls, saving the team 250,000 hours annually.
Analyzing new segments required manually tagging 50K reviews. Now, AI automatically translates and categorizes multilingual feedback.
High call volumes bottlenecked 130+ agents. Now, a secure internal LLM resolves app queries and summarizes chats, even during air raids.
Agents typed furiously while customers waited on hold. AI now summarizes calls, saving the team 250,000 hours annually.
Strict privacy rules prevented sending raw call transcripts to AI. Now, an automated system safely strips and restores sensitive data.
Quarterly compliance updates meant costly reshoots. Now scripts regenerate instantly — no cameras, no vendors, no delays.
Engineers capped at basic chat, complex work out of reach. Now 700 tackle cross-jurisdiction compliance reviews in hours, not 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.