How Thredd uses AI to build software

Thredd harnesses AI to accelerate development, enhance quality, and future-proof operations, driving efficiency, security, and cost savings across its fintech platform.

Edwin Poot

April 02, 2025

At Thredd, AI accelerates development, enhances quality, and future-proofs
operations. From code reviews and vulnerability fixes to incident triage and data Insights, AI drives efficiency, security, and cost savings, positioning our platform at the forefront of fintech innovation.

Revolutionizing Software Development with AI

With our journey toward building a next-generation issuer processing platform we
have embraced Artificial Intelligence (AI) as a key enabler to accelerate development, enhance quality, and future-proof operations. Here is a summary of how we are using AI at various stages of our software development lifecycle:

1
Enhancing Development Velocity and Code Quality
By using AI, we transform our approach to software development through intelligent
automation and augmentation:
- Code Generation and Review: We use tools like Amazon Q to automate code reviews,
  generate test cases, and produce technical documentation, ensuring consistency and
  quality in our Python codebase.
- Static and Dynamic Code Analysis: Security tools like Snyk and Sonar AI CodeFix help
  find vulnerabilities and suggest automated fixes, significantly reducing technical debt
  while keeping compliance with security standards.
- Legacy Code Modernization: While we initially explored using AI-powered tools (e.g.,
  AWS Bedrock) for converting C# to Python, manual efforts proved more efficient.
  However, such tools stay part of our roadmap for future use cases involving legacy-to-
  modern technology transitions.
2
AI-Driven Operations and Support
We also plan to use AI to support our operational excellence by improving incident
management and data handling:
- Incident Triage and Escalation: Large Language Models (LLMs) like those available in
  AWS Bedrock act as "switchboards," aiding in incident triage and escalation by guiding
  first-line support teams to resolution paths or right escalation points.
- Text to SQL Solutions: Leveraging AI for natural language-to-SQL query translation
  accelerates data insights, enabling non-technical teams to interact directly with
  databases via an AI agent interface.
3
Supporting Security and Compliance
We have been Integrating AI-based security solutions to ensure robust protection
throughout the development and deployment process:
- Package and Library Scanning: Snyk finds vulnerabilities in Docker images and code
  libraries, automatically raising remediation pull requests.
- Code-Level Vulnerability Management: Sonar AI CodeFix provides proactive code fixes
  for found vulnerabilities, streamlining secure coding practices

Reducing Development Costs with AI

AI potentially helps us achieve cost savings by automating tasks like code generation, reviews, testing, and documentation.

For example, developing a complex module manually might take 9.5 person-days. With AI tools like Amazon Q, this effort can be reduced to 4.5 person-days — a time reduction of over 50%.

These savings, when multiplied across the platform, allow us to distribute resources more strategically and accelerate our time-to-market.

The strategic integration of AI in our software development process positions our platform at the forefront of technological innovation, aligning with our broader business

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