In 2025–2026, the pressure on software engineering teams is increasingly measured through delivery metrics rather than general statements about speed. According to the 2025 DORA research, based on a survey of nearly 5,000 technology professionals, AI-assisted development is changing how software is built, but its effect depends on the maturity of the underlying engineering system. The report frames AI not as a shortcut, but as an amplifier of existing organizational strengths and weaknesses.
This makes engineering process quality a current business issue. DORA evaluates delivery performance through indicators such as deployment frequency, change lead time and failed deployment recovery time — the same type of metrics companies use to understand whether teams can release regularly and recover quickly after incidents. In software testing, the 2025 Software Testing and Quality Report also shows the connection between automation and release performance: teams with strong test automation and CI/CD integration report faster release cycles in 86% of cases and reduced defect leakage in 71% of cases.
It was in this market context that Evgenii Lvov assumed the position of Head of Development at Aventus, a product-focused IT company operating in the financial technology sector. His task was not limited to accelerating development. He had to rebuild the operating model of the engineering function: make planning more transparent, reduce technical debt and connect engineering decisions with measurable business outcomes.
The transformation initiative commenced with a comprehensive diagnostic audit covering project management systems, operational workflows, and the history of architectural decision making. Evgenii Lvov conducted an exhaustive review of internal communication patterns, system performance data, and the distribution of product ownership. This diagnostic phase was critical because it shifted the focus away from superficial symptoms like release delays or urgent defects. Instead, Evgenii traced these issues to systemic root causes including fragmented knowledge, inconsistent engineering standards, and an increasingly disjointed architectural landscape. This trajectory is a common challenge for rapidly scaling fintech organizations. When architectural maturity and quality controls fail to keep pace with product expansion, departments often drift from strategic delivery into reactive firefighting. Evgenii Lvov’s primary objective was to reverse this trend by grounding engineering decisions in predictable, measurable outcomes. This operational shift forces developers to prioritize the resolution of urgent bugs while business stakeholders suffer from a marked decline in release predictability.
A primary and measurable outcome of the subsequent refactoring phase was a 30 percent reduction in SQL queries across key application scenarios. To accomplish this, Evgenii Lvov led a rigorous review of application logic, database interactions, and complex execution algorithms. His contribution extended well beyond administrative coordination as he participated directly in analyzing system behavior under peak workload conditions to identify where the codebase exerted unnecessary pressure on infrastructure. Following this optimization, system execution times returned to optimal operational levels. This practical resolution served as a powerful demonstration to the engineering division, highlighting the direct correlation between rigorous code quality and overarching system stability.
Parallel to these technical refinements, Lvov executed a strategic overhaul of the team structure. Guided by his audit findings, he focused on constructing cross functional groups to diminish the dangerous reliance on isolated knowledge silos. This structural adjustment was essential for long term stability because distributing expertise across the entire team makes delivery processes significantly less vulnerable to talent turnover or sudden communication gaps.
This personnel strategy fostered a culture centered on shared company outcomes while ensuring that operational workflows remained transparent. Scrum and Kanban were implemented not as rigid labels, but as versatile instruments for restoring planning discipline and visibility. Frequent status meetings and demonstrations of interim results helped stakeholders grasp the rationale behind task prioritization and the direct impact of technical health on product profitability. Furthermore, Lvov established an internal mentorship program designed to cultivate future team leaders from within the existing staff. This framework not only preserved vital technical context inside the company but also created a transparent career trajectory for engineers, which is critical for departments where institutional memory dictates the pace of innovation. Lvov brings a broader industry perspective to his internal work. In 2024, he served as an expert judge for the Edtech, Gaming and Entertainment: What is Next? competition. By evaluating 10 finalists from an initial pool of 97 applications, he sharpened his ability to assess the scalability of ventures, a skill he now applies at Aventus to refine product strategy and integrate new technological directions, including AI-assisted development tools.
Evgenii Lvov also revised the approach to technical debt management. Instead of treating refactoring as an occasional activity, he introduced a rule under which part of each work cycle was allocated to architectural maintenance and improvement. This shifted the team from reactive fixes to planned system strengthening. For a growing fintech product, this approach is important because technical debt can directly affect release predictability, incident recovery and infrastructure costs. This enabled the gradual and systematic strengthening of the system, transforming the development department into a predictable unit capable of handling growing workloads without compromising quality.
Particular attention was given to creating a working environment that encouraged initiative. Engineers were given the opportunity to propose their own ideas for process and system optimization. The introduction of automated testing and quality control systems provided the technical foundation for the new operating standards, all of them aimed at improving the reliability of released updates.
Evgenii Lvov’s actions led to the standardization of processes and prepared the technological foundation for the product’s further expansion into new markets. A systematic approach to knowledge management ensured that expertise remained inside the company rather than being lost through fragmentation or turnover. The Aventus case demonstrates that the effectiveness of a technical leader lies in the ability to build processes that take into account both business goals and the specific realities of engineering work.
The impact of these interventions was reflected in concrete operational metrics. The mean time to recovery was reduced from a window of two to three days down to a rapid six to eight hour cycle. Release cycles that previously consumed one to three weeks were successfully shortened to a cadence of one to three days. Release frequency experienced a corresponding surge as the company moved from 206 releases in 2023 to 273 releases in 2024. This achievement represents 67 additional production releases over the calendar year, which translates to approximately six extra releases per month. Furthermore, the interval between releases decreased from an average of one every 1.8 days to one every 1.3 days. These metrics illustrate a broader principle for fast growing digital companies: sustainable delivery speed is only possible when supported by disciplined engineering processes. The Aventus case demonstrates a broader principle relevant to fintech and other fast-growing digital companies: delivery speed becomes sustainable only when it is supported by disciplined engineering processes. Faster releases alone do not solve scaling problems. What matters is whether the organization can release regularly, recover quickly, preserve knowledge inside the team and improve the system without interrupting product growth.
The distinctive feature of Evgenii’s approach was not the use of refactoring, automated testing or agile methods separately. These practices are already common in the market. What made the case noteworthy was the way they were connected into one operating model: audit findings were translated into engineering standards, standards were embedded into Merge Request workflows, and progress was evaluated through measurable indicators such as test coverage, SQL query reduction, MTTR and release frequency.
This approach may be useful for other engineering teams facing similar scaling problems. It shows that process recovery does not depend on one tool or methodology. It requires a sequence of decisions: diagnose the real causes of instability, standardize quality control, reduce architectural bottlenecks, distribute knowledge inside the team and measure whether the changes affect delivery outcomes.
