Tranzzo is a global online payment platform serving over 3,000 active clients and processing more than 10 million transactions monthly. Operating under strict PCI DSS standards supervision, the company builds infrastructure to support various real-time financial operations.
Project Overview
The Tranzzo team aimed to automate routine code checks to accelerate releases and reduce the workload on the team while maintaining full compliance with security standards.
With the support of Wise IT, a custom AI reviewer based on Gemini in Google Cloud was implemented. This allowed combining the power of LLM with security guarantees and an efficient cost model.
The Business Challenge
The Tranzzo development team faced a challenge: the classic approach to Code Review had turned into a “bottleneck,” slowing down the pace of releases. Business scaling only exacerbated the situation. Staff expansion and architectural complexity created an excessive load on Senior engineers, leading to reduced verification thoroughness. Meanwhile, standard automation tools did not simplify the work, as they did not account for the business logic context.
Tranzzo decided to implement generative AI to improve early error detection and reduce the load on developers. However, as a fintech company, they had strict limitations regarding data security.
The company turned to Wise IT, a trusted Google Cloud partner, for technical expertise and architectural support. The task was to design a system that would use the Gemini API but remain entirely within Tranzzo’s secured perimeter on Google Cloud.
The Solution
Tranzzo, in partnership with Wise IT, developed an in-house AI reviewer based on Gemini 2.5 Pro, deployed entirely within their Google Cloud infrastructure. The implementation process took only three weeks and was divided into iterations, each of which improved the system’s accuracy.
Wise IT’s role in the project involved strategic consulting and technical support. Wise IT experts consulted Tranzzo engineers on such critical issues as:
- Prompt Engineering: Assistance in creating prompts that allow the model to focus on business logic, not just syntax.
- Orchestration: Designing cloud architecture to ensure seamless service operation.
- Cloud-Native Patterns: Using best practices to accelerate Time-to-Value without compromising security or control.
Solution Development Stages
Phase 1: Pilot and basic context
Initially, the system was taught to react to the creation of a pull request (PR), extract code changes (diff), and the task description from Jira. LLM responses were parsed into structured comments.
Phase 2: Deepening context
Based on feedback, the system was expanded:
- Git checkout was added for full project context;
- Diff file processing was optimized;
- A CODE_REVIEW.md file with rules for each project was introduced;
- The AI was taught to consider architectural patterns and test coverage.
Phase 3. Scaling (Week 3+)
The system was successfully deployed across all Tranzzo teams without changing their workflows. Now, every Pull Request automatically undergoes an AI review before the human review.
The Result
In just one month, Tranzzo received a full-fledged code verification tool.
Key metrics:
- 54%+ of AI recommendations are accepted by developers.
- 100% of teams use the tool daily.
- ~30% reduction in time-to-merge.
“AI does not replace human reviewers — it expands their capabilities. Thanks to the automation of routine checks, the system allows engineers to concentrate on strategic and architectural decisions.”
— Yurii Horbyliov, CTO of Tranzzo.
Google Cloud Platform Advantages
Aside from technological achievements, the solution architecture based on Google Cloud provides two fundamental business benefits:
1. Financial Efficiency
The Pay-as-you-go payment model allowed avoiding capital expenditures.
“The Pay-as-you-go approach eliminates the need for significant initial capital investments and provides unprecedented economic flexibility for scaling resource-intensive projects, such as artificial intelligence workloads.”
— Dmytro Kerez, Google Cloud Product Manager, Wise IT.
2. Data Sovereignty and Security
For Tranzzo, as a company operating under strict PCI DSS standards, privacy guarantees were crucial.
“According to Google Cloud’s official commitments regarding privacy in the field of AI and machine learning, your corporate data, queries, and any generated results are never used to train their foundational models. This policy guarantees that the company’s proprietary information and intellectual property remain completely confidential and isolated.”
— Oleksandr Kozub, Head of Cloud Department, Wise IT.
Best Practices
The experience of Tranzzo and Wise IT allowed formulating a number of recommendations:
- Partnership with experts: Engaging an experienced Google Cloud partner is a crucial factor for properly setting up the AI/ML environment, optimizing costs, and adhering to integration best practices.
- Invest in Prompt Engineering: Adapted prompts yield significantly better results than generic queries.
- Start small: A pilot team allows collecting feedback and tuning the solution without risk to the entire organization.
- Balance automation: Leave the final decision to the human. AI handles routine perfectly, but architectural decisions require human understanding.
The Tranzzo and Wise IT project was included in the Google Cloud EMEA SMB AI Playbook
The Tranzzo case on code verification automation was included in the collection of best AI solutions for small and medium-sized businesses (SMB) by Google Cloud. It demonstrates how artificial intelligence can significantly accelerate development, improve code quality, and free engineers from routine checks.
Conclusion
The collaboration between Tranzzo, Wise IT, and Google Cloud proves that strategic implementation of AI can bring measurable value even in highly regulated industries.
Thanks to support from Wise IT, Tranzzo was able to avoid typical mistakes when integrating GenAI, building a solution that analyzes complex pull requests with high accuracy while maintaining full code privacy and operational efficiency.
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