Siberian.pro Accelerated Development by 50%: How AI Agents Transformed the Company's Internal Processes

At Siberian.pro, a company specializing in digital solutions, a decision was made a year ago to actively integrate AI into its proprietary development processes. Today, a year later, analyst productivity has increased by 40-50%, the prototyping phase has been accelerated by several months, and code review time has been reduced by 30%. The company shares its experience on how AI agents have changed its approach to product creation.

Software development involves a lot of routine: compiling technical specifications from unstructured data, creating prototypes, initial code reviews, writing unit tests. All of this consumes hundreds of hours of highly skilled professionals' time, slows down product time-to-market, and increases costs. However, today this routine can be automated, freeing the team for more complex and creative tasks.

How It All Began: From Experiments to Strategy

Like many in 2023, Siberian.pro experimented with large language models (LLMs). Initially, these were more like "games," but by 2025, it became clear that beyond the capabilities of AI lay real business potential. The company's management decided not just to use AI tools in their work, but to create a dedicated AI unit and purposefully integrate AI into development processes, as well as offer AI solutions to clients.

The main question posed to the team was: "Why? What exactly do we want to change?" Any technology should serve the goal of increasing efficiency, not just be a trendy gadget. The goal was not to replace people, but to provide them with tools that would make their work more productive and engaging.

AI Agents in Business and Product Analytics

One of the first areas was the use of AI in analytics. LLMs proved to be an ideal tool for working with raw data.

• Compiling Technical Specifications. The agent analyzes call recordings with clients (even the most unstructured ones), identifies business requirements, risks, and key metrics. The output is a draft technical specification that the analyst refines. This allows for significantly faster generation of even high-level design documents (HLDs).
• Presale Analytics. An AI agent helps quickly grasp the client's market context and requirements to offer relevant solutions even before development begins.
• Qualitative Data Analysis. AI effectively processes customer feedback, quickly identifying the main pain points of the target audience, which is useful for product audits or presales.
• Preparation of Primary Documentation. Information obtained from AI at previous stages is used for automatic generation of documentation for prototypes.

As a result of this approach, analyst productivity increased by 40-50%. For example, on one project, the prototyping phase, which typically takes several months, was completed in 30-40 days.

Accelerated Prototyping with AI Agents

For creating prototypes, including functional ones, AI agents also demonstrated high efficiency. A prototype is needed quickly to test hypotheses, demonstrate concepts to the client, and ensure mutual understanding. Details and nuances are not critical at this stage.

Using AI tools, the team can create a functional prototype in two to three steps. For instance, technical specifications for specialized prototyping tools can be composed with an LLM. This allows the client to see how the future system will work several months earlier than with a traditional approach. It's important to note that such prototypes are not intended for production; their purpose is to quickly validate ideas.

AI Agents in Code Review and Testing

Code Review. Using AI for the first line of code checking has become standard. An AI reviewer can identify up to 99% of critical and important bugs. Deploying such an agent takes mere minutes, and time savings reach 30%. If the AI agent has access to project context, non-functional requirements, business logic, and revision history, automated review becomes more in-depth. Final verification, of course, is still performed by a human.

Testing. AI agents automate all stages of testing: unit tests, integration tests (API verification, HLD compliance), and also partially UI and End-to-end tests. The goal is for QA specialists to only describe test cases, and AI agents to perform automated tests. Already, the speed of test preparation has tripled: where it previously took up to six hours for manual scenario writing, AI allows it to be done in two hours.

The time freed up is spent by QA specialists on more in-depth analysis and manual testing, which AI cannot yet perform independently. Testers note that the backend is easier to cover with tests using LLMs, while the frontend remains more complex for automation. AI agents are also used to create scripts that automate the checking of business logic and feature availability, helping to identify peak loads and failures.

Internal Document Search and Agent Skills

AI agents also found application in company knowledge management.

• RAG (Retrieval Augmented Generation). Siberian.pro has created an internal knowledge base with an AI assistant based on an open-source solution, which answers employee questions about vacations, sick leaves, working with Jira, and internal standards in Confluence. This reduces unproductive communications and speeds up work. This system is planned to be expanded to other work tools. The agent allows for preliminary project estimation based on RAG-wrapped information within an hour or two.
• Agent Skills. This approach, based on precise instructions for LLMs, allows agents to retrieve information from various sources. For example, agents can independently obtain links to documents in Confluence, read them, and get bug information from Jira. This saves LLM context, employee time, and increases agent autonomy, ultimately accelerating all production processes.

Results and Prospects

Over a year of implementing AI agents, Siberian.pro has not only significantly accelerated its internal development processes but also improved product quality. The time freed up is spent by specialists on more complex tasks that require human intelligence and creativity. The company continues to develop its agent-based approach, seeing the greatest potential for transformation in it.

How to Implement This in Your Company

Siberian.pro's experience demonstrates that AI agents can bring significant benefits to development. If your company faces similar challenges, consider the following steps:

• Start with analytics and prototyping. AI agents efficiently process unstructured data for technical specifications and quickly create prototypes, accelerating initial project phases.
• Integrate AI into code review and testing. Use agents for the first line of code review and to automate test writing, which will significantly reduce time and improve quality.
• Create an internal knowledge base with an AI assistant (RAG). This will reduce unproductive communications and speed up access to information for employees.
• Utilize Agent Skills. Define precise instructions for AI agents so they can independently retrieve information from your internal systems (Jira, Confluence, etc.), increasing their autonomy and productivity.

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