Siberian.pro Accelerated Analytics by 50% and Saved Hundreds of Hours: How AI Agents Transformed Development

A year ago, Siberian.pro, a digital solutions development company, decided to move from AI experiments to active implementation. By establishing an internal AI unit, they began systematically applying AI agents in their own business processes, primarily in development. During this period, they managed to accelerate business analytics by 40-50%, reduce code review time by 30%, and significantly improve testing speed and quality, freeing up hundreds of specialist hours for deeper work.

In development, routine is a hidden resource drainer: hours are spent on drafting initial documents, searching for information, typical reviews, and checks that don't create new value. Multiply these hours by the cost of a highly skilled specialist, and it becomes clear how much business loses. Today, this burden can be removed, not just by automating, but by fundamentally rethinking the approach to work.

The Reality of Development Before AI Agent Implementation

Before the active implementation of AI, as in many other companies, a significant portion of specialists' time at Siberian.pro was spent on routine but necessary operations. Business and product analytics required manual extraction of requirements from unstructured customer communications, drafting initial terms of reference (TOR) and high-level designs (HLD). The prototyping process was lengthy and resource-intensive, and code review and testing, although partially automated, still required significant human involvement.

Moreover, conventional automation, based on rigid scripts, only handled formalized processes. Any deviation from the template—a non-standard request, an atypical document—would again fall on the specialist's shoulders. This slowed down the process, increased the likelihood of errors, and diverted valuable personnel from solving more complex, creative tasks.

The Road to AI Agent: From Hype to Systematic Approach

In 2023, when many were still "playing" with neural networks, Siberian.pro was already experimenting with LLMs. However, a year later, it became clear that this was not just a toy, but a powerful tool for business transformation. The company's leadership asked, "Why? To what end?" The goal was not merely to implement new technology, but to enhance efficiency and create higher-quality products, rather than replacing people with machines.

The company realized that for real change, it wasn't enough to simply "attach a touchscreen to a horse." It was necessary to abandon old approaches and transition to a fundamentally new way of working. The AI agent was seen not as just another tool, but as a full-fledged assistant capable of taking on a significant portion of routine tasks and acting autonomously within defined rules.

How the AI Agent Was Designed: Functionality and Roles

At Siberian.pro, the AI agent was designed as a multifunctional system capable of performing a range of key tasks in the development lifecycle:

• Business and Product Analytics. The agent learned to extract business requirements, risks, and key metrics from unstructured data (e.g., call recordings with customers), generate draft TORs and HLDs, and analyze qualitative data (feedback) for pre-sales and product audits.
• Rapid Prototyping. The AI agent gained the ability to generate functional prototypes based on text descriptions, using specialized tools. This allowed for faster hypothesis testing and concept demonstration to clients.
• Code Review. The agent was integrated into the code review process as a first line of defense, capable of identifying up to 99% of critical and non-critical bugs at early stages. With access to project context, non-functional requirements, and revision history, automated reviews became more thorough.
• Testing. The AI agent took on the automation of all testing stages: unit tests, integration tests, and partially UI/End-to-end. The goal is for QA specialists to only describe test cases, while the agent independently performs all automated tests.
• Internal Document Search (RAG). An internal AI assistant was created based on an open-source solution, answering questions about the corporate knowledge base (vacations, sick leaves, Jira usage, company standards). Expansion to project data for quick information access is planned.
• Agent Skills Utilization. The agent was "assigned" skills—precise instructions on how to retrieve and process information from various sources (Confluence, Jira, GraphQL). This increased agent autonomy and reduced LLM context consumption.

A key principle was to keep humans in the decision-making loop. The agent prepares, collects, analyzes, while the human reviews and makes the final decision, focusing on what requires judgment rather than routine.

Implementation: A Phased Approach and Team Engagement

The implementation of AI agents at Siberian.pro was not revolutionary but evolutionary. The company started with areas where benefits were clear and risks were minimal. Gradually, seeing the real time savings and quality improvements, employees themselves began to propose new AI use cases. This ensured high team engagement and organic integration of agents into daily processes.

Special attention was paid to training and adaptation. Specialists did not feel "replaced," but saw the AI agent as a powerful tool that freed them from tedious work and allowed them to focus on more complex and interesting tasks. This approach led to a situation where 80% of employees actively use AI in their work.

Results of the Transformation

The main result is not just hundreds of hours saved and increased productivity. The company was able to significantly improve product quality, reduce development and prototyping times, and enhance employee satisfaction by freeing them from routine. The AI agent did not replace people but made their work more meaningful and efficient.

How to Replicate This in Your Business

The Siberian.pro case demonstrates that AI agents can radically transform approaches to development and project management. If your company has processes where skilled specialists spend time on routine tasks, this is a ready-made scenario for implementation:

• Identify routine points. Find the most common and repetitive tasks that consume the time of analysts, developers, and testers. These could include drafting documents, searching for information, typical code reviews, or writing test scenarios.
• Start with prototyping and analytics. Implementing an AI agent for rapid prototyping and business analytics will yield quick and tangible results, increasing the speed of hypothesis testing and the quality of initial documentation.
• Integrate the agent into familiar tools. The less employees have to change their habits, the faster they will adopt new technology. Embed AI agents into existing platforms and services.
• Leave creativity and decisions to people, preparation to the agent. The AI agent should be an assistant that gathers data, drafts documents, and performs routine checks, leaving the human with the function of decision-making and creative analysis.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager