Unibank: AI Cuts Manual Engineering Effort by 40% Across the SDLC

We embedded Claude Code into the day-to-day engineering work of one of Azerbaijan's leading private banks — from requirements analysis through to code review and testing — and manual effort across the covered lifecycle activities dropped by 40%. In four weeks, the bank moved from experimenting with AI to running standardized, AI-assisted workflows in production. This article covers a successful case, and we're happy to share all the details of the process with you.

About the Client

Unibank is one of the largest private banks in Azerbaijan, headquartered in Baku and serving retail and business customers across the country. It is among the region's leading private financial institutions, with a large in-house engineering organization delivering and maintaining the bank's software under established development standards and governance.

The Problem

The bank's engineering teams work to a defined delivery process, and that process keeps growing. Requirements have to be analysed and refined into user stories. Technical specifications and documentation have to be written and kept current. Code has to be reviewed. Test coverage has to be maintained. Each step is necessary, each is largely manual, and together they consume a significant share of engineering capacity that could otherwise go into building product.

Most engineering organizations recognize this pattern. The question is not whether AI can help with it, but how to introduce AI into an existing development lifecycle without breaking the standards and controls the organization already depends on. Sound familiar? Keep reading.

How the Project Was Organized

The project ran over four weeks, in three phases.

• Phase 1 — Assessment and Planning (Week 1). We assessed the existing software development processes and identified where AI automation would realistically pay off. Activities were prioritized by potential productivity gain, and we defined the implementation roadmap, success criteria, and concrete engineering use cases. The week closed with a first set of standardized prompts and workflow templates.
• Phase 2 — Implementation Across the Lifecycle (Weeks 2–3). Claude was implemented to support the prioritized engineering processes, and pilot adoption began with the engineering teams. Workflows were refined continuously against practical usage rather than assumptions.
• Phase 3 — Optimization and Operational Adoption (Week 4). Prompts and AI-assisted workflows were optimized based on engineering feedback. Adoption, productivity improvements, and process effectiveness were measured; best practices and reusable templates were refined. The phase ended with knowledge transfer, user enablement, operational documentation, and full transition to business-as-usual operation.

What We Built

A set of AI-assisted engineering practices, built on Claude Code, covering:

• Requirements analysis and user story refinement — turning raw requirements into structured, reviewable stories
• Technical specification and documentation generation — specs and documentation produced alongside the work instead of after it
• Code generation and code explanation — including fast onboarding onto unfamiliar parts of the codebase
• Pull request and code review assistance — a consistent first pass on every pull request before human review
• Unit test and test case generation — coverage extended without proportional manual effort
• Knowledge retrieval and troubleshooting support — answers grounded in the bank's own technical context

Alongside the tooling, the bank received a library of standardized prompts and reusable workflow templates, so the practices stay repeatable across teams rather than living in individual engineers' habits.

Results

Measured at the close of the engagement, manual effort across the covered lifecycle activities fell by 40%. Requirements refinement, technical documentation, code review, and test generation each now run through a standardized AI-assisted path, and the time engineers previously spent producing that work by hand has been returned to product delivery.

AI-assisted workflows now operate as part of the bank's normal development process, not as a side experiment, with prompts and templates owned by the bank's own teams. The workflows have been live in production since August 2026.

Established development standards and governance stayed intact. Claude accelerates the work that leads up to an engineering decision; the team still makes it.

Closing Thoughts

AI in the development lifecycle tends to fail for one of two reasons: it is introduced as a tool with no workflow around it, or it is introduced in a way that quietly bypasses the controls the organization relies on. This rollout avoided both — assess first, standardize the workflows, pilot with the people who will actually use them, then optimize on real feedback.

If your engineering organization is facing the same question, get in touch with us — we'd be glad to talk through how a similar rollout could work for your teams.

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