AI-Augmented Software Development Services
The drive to automate some aspects of software development has been around for about as long as programming itself, but with the advent of AI-powered coding tools, it’s now become mainstream… and beneficial. AI can now help developers ensure high standards throughout the SDLC – provided they know how to use it wisely.
Discover how our AI-augmented development services enable us to deliver robust, safe, and unique software faster, and in what cases they are most useful.
What is AI-augmented development?
By definition, AI-augmented software development is the practice of using AI tools as auxiliaries in parts of the software lifecycle (code generation, testing, debugging, documentation, and so on). And yet, when a developer simply uses something like ChatGPT or GitHub Copilot for tricky moments, this is far from an organized approach that we have in mind.
What matters is being consistent and systematic in understanding when and where to use artificial intelligence responsibly instead of just delegating tasks to save on the team. With proper transparency about generative AI, the developer’s role is redefined, but becomes even more important and impactful, while the software is produced faster and more efficiently.
Why would you need to enhance a development team with AI tools?
One of the main reasons is the speed of development. There are several types of tasks that are notorious time-drainers, like unit test writing, debugging complex logic, or practically anything that concerns legacy code. With AI technology, these are streamlined for normalized time-to-market.
Secondly, it’s the general “shift-left” trend, where security, testing, and deployment are tested earlier on, which means the developers need to juggle more factors at once than possible for unassisted human intelligence.
Finally, modern systems are becoming more diversified and complex, and often exceed what one can manually optimize, so that AI is a welcome toolkit, which can also serve as a knowledge base for industry-specific best practices.
USE CASE EXAMPLES
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Boilerplate code and CRUD generation
Automatically create repetitive code structures like CRUD operations to boost initial development and reduce human error.
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Automated tests (Unit/E2E) and visual regressions
Generate and run automated test suites, detect UI inconsistencies, and ensure code changes don’t break existing functionality.
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Migrations and refactoring with hints
AI assists developers in safely migrating legacy systems and refactoring code by offering smart suggestions and detecting dependencies.
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Documentation and alerting
Auto-generate technical documentation, keep it up to date, and trigger alerts about performance issues or anomalies.
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Data and analytics (SQL-guided prompts, LLM agents)
AI can help query databases using natural language, uncover insights, and build intelligent analytics workflows powered by language models.
TOOLS
GitHub Copilot
Codeium
Cursor
SonarQube
Snyk
Dependabot
GitHub CodeQL
OWASP ZAP
Benefits and insights
Resource and time optimization
Since the more routine tasks (including documentation) can be relegated to AI, the engineer can focus on complex logic and design. This creates a double-pronged effect: on the one hand, the automated tasks are done more quickly, on the other, the overall approaches and problem-solving are more efficient and creative, allowing to further reduce the effort to put in.
Fewer post-release defects
One of the most well-documented advantages is that possible issues are caught earlier in the pipeline: vulnerabilities, edge cases, and the like. This, in turn, means fewer bugs make it into production, and the maintenance costs are reduced accordingly.
Higher code maintainability
AI-assisted refactoring and documentation features ensure codebases remain consistent and easier to understand across teams and iterations. The result being, the solution is easier to scale or update later on; migrations are also easier.
Our Software Development Process
DISCOVERY
We translate business needs into tech requirements, assess feasibility and risks, and form a roadmap. Artifacts include a BRD, technical proposal, UX wireframes, roadmap, and estimates.
PILOT
Here, a prototype or MVP is created to validate the value proposition and resolve early issues after feedback. Apart from MVP, deliverables include pilot testing analytics, workable backlog, and revised architecture/UX.
ROLLOUT
The main bulk of implementation work is at this stage: development, deployment, and adoption phase. The goal is to make the solution stable, secure, and fully functional, integrate it and ensure adoption. By the end of this stage, there is a production-ready codebase, documentation, manuals, and DevOps pipelines.
CONTINUOUS IMPROVEMENT
This is where the important further refining takes place: alongside maintaining performance, the solution is honed to fit the realities of usage. The tools used are regular usage reports, updated roadmaps and backlogs, patching, and more.
Our Approach
Audit trails and accountability
We view AI as an assistant and emphasize human supervision and mediation for accountability reasons. Once it is clear who generated what and when, it is possible to ensure not just productivity but also better quality of AI-generated code, which is then controlled in mandatory code reviews.
Security and reliability by design
Our AI-assisted workflows combine development tools like Copilot, SonarQube, Snyk, and CodeQL to keep performance, security, and code integrity at the highest level. With strict policies covering quality control, data security, licenses, and IP protection, we ensure every AI-powered contribution meets enterprise-grade standards.
Clear and formalized governance
We strive to include AI use as a mature software engineering practice and not a series of sporadic episodes. Systematic, ISO-style governance helps us make sure AI has its designated place in SDLC while we keep an eye on compliance and make the right choices along the way.
PRICING MODELS
Time and Materials
Reimbursement for the actual hours and resources used (most flexible model).
Retainer model
A fixed monthly fee for ongoing access to a dedicated team and expertise on the project.
Fixed-scope pilot
A clearly defined project with fixed deliverables and budget to validate a concept.
To find a perfect solution
