SUCCESS STORY

Hybrid
enterprise AI
platform for
analytical autonomy
and onboarding

We built an enterprise AI platform on Azure with an Onboarding Agent for a large banking institution in the US. The solution reduced false positives by 90%, accelerated data analysis to 30 seconds, and reduced operational costs to ~0.20 USD per case.

Azure OpenAI
AWS Bedrock
LiteLLM

Results

in false positives in onboarding analysis
- 0 %
in average analysis time
0 s
in operational cost per analysis (10x lower than manual)
~0, USD

Direct reduction of CAC and the end of manual data cross-referencing

Overview

A major US banking institution faced bottlenecks in onboarding and compliance, dealing with a high rate of false positives and slow data validation. Operational friction consumed intensive technical hours, requiring an evolution in the efficiency of the compliance engine without altering the legacy architecture while maintaining maximum data security.

The challenges

High rate of false positives and slowness in the onboarding and compliance process.
High operational friction and heavy consumption of technical hours for manual validation.
Need to evolve the current engine while keeping the legacy architecture intact.
Strict security requirements and mitigation of regulatory risks in raw data

Journey
and solutions

We built the bank’s first AI Competence Center solution, implementing a hybrid enterprise platform on Azure. We developed an agnostic Onboarding Agent that cross-references raw data with banking rules via RAG (Retrieval-Augmented Generation) and API queries. The web interface with human validation (Human-in-the-Loop) accelerates complex analyses without exposing internal infrastructure.

What did we deliver?

Hybrid Enterprise AI Platform structured on the Microsoft Azure cloud.
Agnostic Onboarding Agent with RAG architecture and automated API queries.
Structured web interface for analytical validation (Human-in-the-Loop).
LLM integration (Azure OpenAI and AWS Bedrock) with full data isolation.

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