Project Summary
Company
Private Fund
Industry
Financial Services
Services
AI Software Development
Location
London, United Kingdom
A London-based private fund initiated a pilot project to explore the potential of AI agents for modernizing legacy systems. As a proof of concept, they focused on utilizing AI to migrate a Perl-based codebase to Python 3. This initiative aimed to assess the viability of AI-driven solutions for broader technological innovation within the organization.
Our Solution Delivered:
A successful proof of concept showcasing the efficiency of AI agents in automating complex code migrations.
A modern Python codebase enabling future integration of AI-driven technologies.
Improved efficiency, achieving over 80% time savings compared to manual migration efforts.
The Challenge
The client, a private fund based in London, faced the dual challenge of modernizing its legacy infrastructure and exploring the potential of AI to enhance its operations. While Perl had been part of the fund’s systems, it was increasingly seen as a barrier to adopting modern technology.
The pilot project was framed with two key objectives:
Proving AI's Potential: Demonstrating whether an AI agent could tackle a technically demanding task like Perl-to-Python migration efficiently and accurately.
Laying the Foundation for AI Adoption: Ensuring the resulting Python 3 codebase would align with the fund's strategic vision to integrate AI-powered tools for advanced analytics, automation, and decision-making.
The choice of Python was deliberate, as its extensive AI libraries and community support make it an ideal environment for future innovation.
The Approach
To address the challenge, our team designed and deployed a custom AI-driven solution tailored for Perl-to-Python 3 conversion.
Custom AI Agent Development: We created a pipeline where an AI agent leveraged Perl-to-Python converter outputs as a foundation.
Iterative Refinement with LLMs: A large language model (LLM) reviewed, refined, and optimized the converted code for syntax accuracy and logical consistency.
Automated Testing and Execution: Each segment of the translated code was automatically executed and tested. If errors were detected, the LLM analyzed the feedback, implemented adjustments, and re-executed the code.
Scalability in Conversion: The system divided larger files into manageable segments, ensuring smoother processing and validation.
The Impact
Accelerated Migration: The custom AI agent reduced code migration time by over 80%, significantly cutting down the timeline compared to manual methods.
Easier Maintenance: The resulting Python 3 codebase offers improved readability and maintainability, empowering the fund to implement new features and integrate modern tools seamlessly.
Future-Ready Technology: With the modernized system in Python 3, the fund is now better positioned to adopt advanced analytics, AI models, and other financial technologies.
Proven AI Value: The fund gained confidence in AI’s ability to solve complex challenges and has identified further opportunities for AI deployment in areas such as algorithm optimization, risk analysis, and operational efficiency.
By combining technical innovation with strategic foresight, this pilot project demonstrated the transformative potential of AI agents. The success of this initiative positions the fund to lead the way in leveraging AI for competitive advantage in financial services.
