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RAG-Powered Process Automation for a Fintech Platform

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A RAG-powered automation layer that enables teams to retrieve accurate information, automate internal workflows, and reduce manual processing across finance and operations.

Industry:

FinTech

Location:

London, UK

Duration:

3 months

Budget:

$15K

Case study

Our Approach

We approached this project as a FinTech-grade AI automation initiative, where accuracy, traceability, and security were as important as AI capability.

Our approach focused on: Designing a Retrieval-Augmented Generation (RAG) architecture to ground AI responses in verified internal data.

Automating repetitive operational workflows using AI-assisted decision support.

Ensuring the solution aligned with UK financial and compliance expectations.

Delivering incremental value through automation, rather than replacing core systems.

Instead of building a standalone AI feature, we embedded RAG directly into existing workflows to act as an intelligent operations layer.

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The Problem

As the FinTech platform grew, teams faced several challenges: Key information was fragmented across databases, internal documentation, and reports.

Operations and finance teams spent significant time manually querying data and preparing summaries.

Compliance-related questions required cross-checking multiple sources.

Existing tools were powerful but not easily accessible to non-technical users.

The client needed a way to automate data retrieval and decision support without exposing sensitive systems or introducing compliance risks.

RAG-Powered Knowledge & Data Retrieval

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AI-Assisted Process Automation

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Secure, Controlled AI Access

RAG-BASED AUTOMATION SOLUTION

We delivered a RAG-based automation solution designed specifically for FinTech operations:

KEY FEATURES IMPLEMENTED:
  • 01
    RAG-Powered Knowledge & Data Retrieval

    We implemented a RAG pipeline that retrieves structured data from internal databases, operational documentation and policies, and process-level metadata. The AI generates responses and summaries strictly grounded in retrieved sources, ensuring accuracy and auditability.

  • 02
    AI-Assisted Process Automation

    The system supports automation of internal operational queries, finance and reconciliation checks, and compliance-related information requests. This reduced manual effort and response time for recurring internal requests.

  • 03
    Secure, Controlled AI Access

    We implemented strict controls to ensure role-based access to data, separation between AI reasoning and data execution layers, and traceable outputs suitable for regulated environments.

  • 04
    API-First Integration

    The solution was integrated into existing systems via APIs, allowing teams to access AI-powered automation without changing their core workflows.

  • 05
    Production-Ready Architecture

    The system was designed with scalability in mind, clear logging and monitoring, and readiness for future expansion into customer-facing AI features.

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Danylo MelnychukCEO at Xedrum
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