AI-Assisted Software Development

Chudovo engineers build web, desktop, mobile, and cloud applications by combining traditional engineering and an AI-assisted approach. The outcome: faster software delivery with full engineering oversight and adherence to the best coding practices.
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Our AI-Assisted Software Development Services

Our Awards and Recognitions

Top Software Developers in USA 2026 by Techreviewer
Top Software Development Company 2026 by Feedbax
Top AI Development Company 2026 by Feedbax
Top Implementation Service Company by Goodfirms
Sortlist Trusted Partner
Top Software Development Company 2026 by RightFirms

Why Choose Chudovo for AI-Assisted Software Development

  • Chudovo has been developing software projects since 2006. AI-assisted development is a logical extension of our existing engineering culture and delivery methods
  • Our engineers have hands-on experience in similar projects and actively apply this
  • Our developers have full engineering oversight over the code and processes
  • We follow the best development practices, security standards, and deliver industry-conforming solutions
  • The AI-driven approach helps reduce time-to-market
  • We offer a free consultation and assessment of your project in terms of features, costs, and timelines
  • The project can start within a week after the request
  • Ability to take over fixed price projects with clearly defined scopes

 

How Much Does AI-Assisted Development Cost vs. Traditional Development?

AI-enabled engineering reduces total delivery hours for an equivalent scope. The table below shows the comparison between the pure traditional approach and AI-assisted delivery across common project types.

Project Type Traditional Estimate AI-Assisted Estimate Time Saved Cost Saved
MVP / internal tool (simple, 1-3 integrations) 10-12 weeks / $25,000-$30,000 5-7 weeks / $10,000-$15,000 30-40% 50-60%
Mid-size web or mobile application 4-5 months / $25,000-$30,000 2-3 months / $12,000-$18,000 25-35% 40-50%
Legacy system modernization (mid-size) 5-9 months / $30,000-$35,000 3-6 months / $15,000-$20,000 30-40% 45-50%
Test coverage introduction (legacy codebase) 6-10 weeks / $15,000-$20,000 2-3 weeks / $5,000-$8,000 40-50% 60-70%
API integration layer (3-5 integrations) 6-10 weeks / $25,000-$30,000 2-3 weeks / $15,000-$138,000 25-35% 50-75%
Small enterprise platform 8+ months / $70,000+ 6+ months / $40000+ 20-30% 45-60%
Enterprise platform 10+ months / $90000+ 8+ months / $50000+ 20-30% 45-60%
Large enterprise platform 12+ months / $120,000+ 10+ months / $80000+ 20-30% 45-60%

What Our Experts Say

Dmytro Chudov CEO & CTO
AI-assisted development changes the approaches of what good engineering looks like. The modern teams apply it within the process, but engineers' oversight remains as important as always. The main thing that changes is the speed at which engineers can move through routine work and progress. We treat AI-enabled development as a supportive tool that needs to be configured correctly, governed, and continuously evaluated for where it actually helps.
Dmytro Chudov
CEO/CTO

Technology Stack

AI Developer Tooling
AI-Assisted Code Transformation and Refactoring
AI-Augmented QA and Testing
Process Intelligence and AIOps
AI Workflow Automation
Synthetic Data
Front-End
Mobile
Databases
Observability
AI Developer Tooling
  • GitHub Copilot Enterprise
  • Visual Studio IntelliCode
  • Amazon Q Developer
  • Gemini Code Assist
  • Cursor
  • WindSurf
  • JetBrains AI
  • Tabnine
  • Claude Code
  • ChatGPT 
  • DeepSeek
AI-Assisted Code Transformation and Refactoring
  • OpenRewrite
  • Moderne
  • IBM Watsonx Code Assistant for Z 
  • Amazon Q Developer 
AI-Augmented QA and Testing
  • Tricentis Tosca
  • Applitools
  • mabl
  • Functionize
Process Intelligence and AIOps
  • Celonis Process Intelligence Graph
  • Datadog AI
  • Dynatrace
AI Workflow Automation
  • n8n
  • Zapier
  • Make
  • UiPath Platform
  • Microsoft Power Automate with AI Builder
Synthetic Data
  • MOSTLY AI
  • Syntho
  • Tonic Structural
Front-End
Mobile
Databases
  • PostgreSQL
  • Microsoft SQL Server
  • MySQL
  • MongoDB
  • Redis
  • DynamoDB
  • Docker
  • Kubernetes
  • Helm
  • GitHub Actions
  • GitLab CI/CD
  • Azure DevOps
  • Terraform
  • ArgoCD
Observability
  • Prometheus
  • Grafana
  • Datadog
  • ELK Stack
  • OpenTelemetry

AI Security and Data Privacy

The use of enterprise AI-assisted development poses unique challenges around security and data governance. We handle these concerns during the tooling and architecture phases. We have engineering oversight and internal reviews built into our processes to help ensure security and governance.
Private Codebase and Data Isolation
We configure the environments to make the code isolated from the external model training pipelines.
On-Premises and Hybrid Deployment Options
For those organizations that require strict data residency, we support on-premises and hybrid configurations.
Role-Based Access Control
We configure role-based access rules with permissions. The engineers have access only to the systems they need to work on.
Audit logs and Compliance
We implement audit trails/logs and support compliance documentation for regulated industries.
IP and License Protection
We implement measures to identify licensing issues and detect code similarities.

Customer's Reviews

Olha Chura
Partnership Manager
Kitrum
The main goals were to ensure stable operation of the existing lending platform, provide continuous maintenance and support, and later modernize the system by separating legacy components, and transitioning to a microservices-based architecture to improve scalability and performance. We were impressed by the developer’s technical expertise, adaptability, and proactive approach to contributing to the project. Chudovo’s ability to provide talent that seamlessly integrates into existing teams and contributes to complex modernization efforts made the cooperation particularly effective.

The Value We Bring Using an AI-Assisted Development Approach

There are major gains in performance, quality, and engineering productivity achieved with AI-assisted development. The numbers shown below are based on the actual engineering experience and industry data.
benefits
30–50% decrease in time spent on repetitive coding tasks
AI tools cover repetitive tasks through automation and help software engineers to focus on architecture, business logic, and other important development aspects.
benefits
2x faster test coverage preparation
The use of AI-driven tools speeds up test writing and achieves higher coverage within half the time compared to writing tests manually.
benefits
Faster ramp-up for complicated codebases
Automated code explanations as well as contextual AI assistance help software engineers understand complex codebases efficiently.
benefits
Earlier bug detection
AI assistance helps detect problems in code earlier. In a long-term perspective, it saves costs related to bug detection and handling after the production stage.
benefits
Faster release cycles
AI-assisted processes in CI/CD make the releases more consistent and faster.

Featured Projects

FAQ

What is AI-assisted development? Answer
It is software development in which AI-assisted tools cover repetitive tasks but stay under full engineering control. This approach helps speed up the dev process itself and improve the quality of the deliverables. The responsibility of architecture decisions, code reviews, and coding quality standards lies with the engineering team.
Does AI-assisted development cut costs? Answer
Yes, most of the time. It reduces the time engineers spend on routine tasks like basic coding, documentation, test generation, and others. It helps reduce total delivery hours for the equivalent scope of manual work. The reduction varies by project type and team experience with the tooling.
How do you protect proprietary code and data? Answer
We set up AI-assisted development in a secure enterprise-grade environment. The customer’s code and data are isolated and cannot be used by models for training.
Is AI-generated code safe for production? Answer
AI-generated code is subject to the same review, testing, and deployment as code written by humans. We do not deploy any AI-generated output; our engineering team reviews the results and adjusts the code to align with best practices, requirements, security policies, and the architecture of the project.
When does the team begin? Answer
Within one week of the first request.
Is it possible to apply AI-assisted development to an ongoing project? Answer
Yes. It’s possible to introduce AI in any particular aspect of a project. We make an assessment of your current project status and its codebase and provide recommendations on how to make it efficient.
Are you working in an industry that imposes some restrictions on the application of AI-assisted development? Answer
Yes. Our clients include those from the healthcare, financial, or public sectors. In this case, there are certain restrictions imposed due to privacy laws and audits.
Do you use AI in code reviews? Answer
We use AI tools for intelligent code reviews to find inconsistencies, potential vulnerabilities, duplicates, or typos.