AI augmented development services for faster, smarter software delivery

Boost engineering productivity, ship features faster, and test new ideas with less risk and cost with our AI-augmented development services. We use AI to handle the routine, while our senior engineers drive every decision that matters.
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20+

years in software engineering

270+

projects delivered

4.9

rating on Clutch

Abto Software combines 20 years of software engineering experience, a team of 300+ specialists, and deep AI expertise. We launched our Computer Vision lab in 2012, and today our dedicated AI/R&D team of 50+ experts helps businesses across industries turn emerging technologies into practical solutions and new growth opportunities.

That strong AI foundation gives our senior engineers an edge in AI-augmented development: they know where AI speeds up delivery and where engineering judgment has to lead.

More code isn’t the goal. More value, delivered faster, is.

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When you need AI-augmented software development

Development can’t keep up with the roadmap

Your team is stretched thin, but the feature backlog is only piling up and release dates are constantly pushed back. The time-to-market remains too long to keep pace with business demands.

Product experimentation is too slow or costly

Building MVPs and PoCs takes too much time and engineering effort. Every new idea becomes a costly bet, and by the time it’s ready for validation, the market may have already moved on.

AI-built features crack under real use

AI-generated code that passed every demo starts failing in production. Edge cases, unexpected inputs, and growing user loads expose hidden flaws and architectural shortcuts.

AI tools aren’t improving delivery performance

Your team writes code faster with AI coding assistants like GitHub Copilot, Cursor, and Claude Code. But releases ship at the same pace because reviews, integrations, and testing haven’t sped up at all.

Legacy modernization feels too big to start

Your systems need an upgrade. But with millions of lines of code, decades of business logic, and tangled dependencies, modernization looks like a multi-year project, so it keeps getting postponed.

Every developer uses AI its own way

One developer works in Cursor, another pastes code into ChatGPT, and a third avoids AI altogether. Without shared AI workflows and standards, quality remains unpredictable and gains are impossible to scale.

What you get with AI-augmented software engineering

Shorter time-to-market

Features move from idea to production faster with AI-assisted coding, test generation, and reviews. Architecture, critical logic, and key technical decisions remain in experienced hands, so you get shorter release cycles without sacrificing quality.

Rapid PoCs and MVPs

With AI-augmented software development, you can validate new ideas in weeks rather than months. AI accelerates prototyping, interface scaffolding, and iteration, while engineers build a scalable foundation, so successful MVPs can evolve into full-fledged software solutions.

Production-ready code

AI augmented development pairs AI efficiency with senior engineering expertise, so you get the speed of AI without the fragility of vibe coding. Every feature is built to hold up in production.

Efficiency across the SDLC

AI-augmented software engineering accelerates more than just coding. AI streamlines planning, testing, code reviews, and deployment preparation, while senior engineers guide the software development process and make critical decisions.

Faster legacy modernization

AI isn’t just for building new software solutions. With AI-augmented development services, you can modernize legacy systems faster and with less disruption to your processes. Experienced engineers stay in control at every step, keeping the transition safe and predictable.

A lasting practice

AI-augmented development can grow into a standard practice across your team, where every developer works with AI in the same effective way and individual wins turn into consistent, company-wide results.

Traditional development vs. vibe coding vs. AI-augmented engineering

Traditional developmentVibe codingAI-augmented development
Approach Engineers develop software products using established practicesAI generates code from natural-language promptsEngineers lead development, with AI assisting across the SDLC
Delivery speedDepends on team size and manual effort Fast at first, slows as issues pile upAccelerated across the whole SDLC 
Code qualityEnsured through code reviews and automated testing  Not systematically ensured: code is accepted as generated Ensured through AI-assisted checks, automated quality gates, and senior review 
TestingTests written manually by engineers and QA Minimal or missingTests generated with AI and validated by engineers 
SecuritySecurity scanning tools plus engineer review Rarely reviewed or scanned; vulnerabilities can slip throughAI-assisted scans plus expert review
ScalabilityPlanned by engineers as part of architecture design Rarely considered upfront Planned by senior engineers, with AI-assisted load testing and performance analysis 
Human controlFullMinimalFull, with AI as a tool
Best forProjects with strict limits on AI technologies use Quick prototypes and personal experiments Fast, production-ready delivery at scale 

 

Our AI-augmented development services

AI-augmented custom development

We build custom products — mobile and web applications, enterprise systems and SaaS platforms — using AI-assisted engineering to accelerate development. Our senior engineers lead the technical decisions, so you get production-ready software delivered faster.

MVP and PoC development

We turn new ideas into working PoCs and MVPs, using AI-assisted engineering to cut build time and cost. Our team helps define the core scope, validates technical feasibility, and delivers a product ready to test with real users or present to investors.

Vibe-coded application rescue

Our AI-augmented software development services also cover rescuing apps built quickly with AI tools that now struggle under real use. Our team audits the codebase, fixes architecture and security issues, adds tests, and brings your product to a production-ready state.

AI-assisted testing and quality engineering

We use AI to generate test cases, expand coverage, and automate regression testing, so quality keeps pace with faster development. Our engineers also run AI-assisted code reviews and audits to uncover areas for improvement.

AI-augmented DevOps and CI/CD

We apply AI to streamline CI/CD pipelines, troubleshoot build failures, and automate routine DevOps tasks, so releases run smoothly as delivery speeds up. Our DevOps engineers also use AI to monitor systems and catch issues early.

Legacy modernization with AI

AI helps us analyze large legacy codebases, map dependencies, uncover hidden business logic, and generate tests before any changes begin. Our engineers then plan the migration, refactor or re-engineer critical components, and move your system to a modern stack step by step.

AI-augmented development team

Our senior engineers can join your existing team, bringing AI-assisted development practices and tools into your workflow. They work alongside your in-house developers and adapt to your processes, increasing engineering capacity and speeding up delivery.

AI consulting for in-house teams

We also offer consulting services for engineering teams that want to make AI a structured part of their development workflows. Our experts assess how your developers use AI today, design a tailored approach for consistent, repeatable results, and train your team to apply it in their daily work.

Get started in three simple steps

1
Share your challenges
Tell us about your product, engineering bottlenecks, and what you want to achieve. We’ll discuss where AI-augmented development can bring the biggest impact.
2
Get a tailored proposal
We’ll assess your requirements and recommend the right engagement model, with clear scope, team setup, and success metrics.
3
Start building faster
Our engineers will join your project, bringing AI-assisted workflows and senior expertise into your development process.

Comparing common cooperation models

Every business has different engineering needs and delivery priorities. We offer flexible engagement models for software development with ai-augmented workflow, so you can choose the option that best fits your setup.

AI-augmented team extension

You extend your in-house team with senior engineers who use AI-assisted development tools and practices to increase delivery capacity.

You define:

  • Required specialists and skills
  • Project scope and priorities
  • Project timeline

Our engineers work alongside your developers and report directly to your team leads.

Key benefits:

  • Access to senior engineers
  • Increased engineering output
  • Integration with your processes

Dedicated AI-augmented team

You get a full dedicated team of senior engineers, QA specialists, and other experts who use AI-assisted workflows to accelerate development.

You define:

  • Product roadmap
  • Project requirements and priorities
  • Delivery milestones

We assemble the team and coordinate daily development. You stay involved through regular progress updates and reviews.

Key benefits:

  • Faster roadmap execution
  • Flexible team capacity

Full-cycle AI-augmented development

You entrust your project to our team that combines AI-assisted development with proven software engineering practices.

You define:

  • Product vision
  • Budget and timeline expectations
  • Acceptance criteria

We handle the entire SDLC, from requirements analysis and architecture to coding, testing, and deployment, using AI to speed up the process.

Key benefits:

  • Faster end-to-end delivery
  • Less project management overhead
  • Reduced project costs

Our featured AI projects

AI-powered indoor navigation at scale

AI-powered indoor navigation at scale

PyTorch · OpenVINO · Kubernetes · AWS · Terraform · RabbitMQ · KServe · Airflow

An indoor mapping company needed scalable infrastructure for accurate positioning across airports, train stations, museums, and other venues. Our team modernized the AI inference service that matches visitor photos to scanned building models. We also refactored legacy code and introduced DevOps and MLOps practices to accelerate releases and monitor positioning accuracy.

  • Reduced risk of false positives in large, similar-looking spaces
  • On-demand scaling for faster responses at lower cost
  • Faster development and more frequent releases
Read full case study
AI-powered indoor navigation at scale
AI text-to-animation for game developers

AI text-to-animation for game developers

T2M-GPT · PyTorch · Python · Django · React.js · Three.js

A game assets provider wanted to help indie developers create humanoid animations without motion capture hardware. Our team integrated T2M-GPT into an MVP that converts text prompts into game-ready animations. We also trained a custom ML model to detect faulty outputs and applied post-processing algorithms to correct them.

  • 2x more subscriptions within a month of release
  • MVP delivered in about 5 months
Read full case study

AI mood board generator for designers

AI mood board generator for designers

CLIP · BLIP · ChatGPT · Python · .NET · Angular · AWS

A US industrial design and engineering company set out to speed up mood board creation. We built a SaaS platform that searches for matching visuals and generates mood boards based on text descriptions, images, and preferred styles. ChatGPT helps refine suggestions, while a Chrome extension lets designers save images from the web.

  • Up to 50% less time spent on mood board creation
  • One-click mood board generation
  • Scalable cloud infrastructure for future growth
Read full case study
AI mood board generator for designers

Client’s review

Abto helped us to create CICD and MLOps pipelines. The CICD pipelines build and integrate our Unity project with a React Native mobile app, which is a complex architecture to automate. We have successfully deployed much of Abto’s work to production and have reduced significantly our costs related to maintenance or support issues.

Read the full review

Charlie Meredith
CTO, Navigation Company

Technology stack

AI ML frameworks

AI ML frameworks

  • PyTorch
  • TensorFlow
  • Keras
  • Scikit-learn
  • OpenCV
  • OpenVINO
  • Apache Spark (MLlib)
  • AWS Machine Learning
Models and LLMs

Models and LLMs

  • GPT models
  • CLIP
  • BLIP
  • T2M-GPT
AI coding assistants

AI coding assistants

  • Claude Code
  • OpenAI Codex
  • GitHub Copilot
  • Cursor
  • Gemini Code Assist
  • Amazon Q Developer
AI code review and testing

AI code review and testing

  • Qodo
  • CodeRabbit
  • SonarQube
  • Snyk Code
  • Playwright
DevOps and MLOps

DevOps and MLOps

  • Kubernetes
  • Terraform
  • Helm
  • KServe
  • Prometheus
  • Grafana
Why сhoose our team

Why сhoose our team

In-house R&D lab

50+ computer vision and AI experts with backgrounds in math and physics

300+ employees

86% of specialists with a Master’s in engineering

AI results across industries

AI solutions with measurable outcomes in healthcare, biotech, logistics, and manufacturing

Recognized AI leadership

Top AI developer on Clutch. One of Eastern Europe’s AI and computer vision leaders

FAQ

AI augmented development integrates artificial intelligence into conventional software development. AI assists with code analysis, coding, testing, and documentation, reducing repetitive work and freeing up engineers to focus on architecture and complex problem-solving.

AI augmented development uses AI tools to build software faster and improve code quality. Engineers rely on AI for coding, testing, code review, and documentation, but the final product doesn’t have to include any AI capabilities.

AI software development, by contrast, means you integrate AI or AI agents into the product itself, such as chatbots or recommendation engines. In short, AI-augmented development uses AI to create software, while AI development creates software powered by AI.

Artificial intelligence can support almost any software project, from building new products to improving existing ones. The most common applications of AI include:

  • Custom application and SaaS development
  • Rapid PoC and MVP creation
  • Legacy system modernization
  • Stabilizing vibe-coded applications
  • Test automation and quality assurance
  • Code review, audits, and documentation
  • DevOps and CI/CD automation

In each case, AI delivers the best results when experienced engineers guide how it’s used.

Security is a real challenge in AI-augmented development. AI tools can expose sensitive code or suggest unsafe solutions if used without control.

An experienced AI-augmented software development company understands these risks and knows how to manage them. With the right safeguards, AI can be integrated securely into development environments and engineering workflows. They include:

  • Private or vetted AI models for sensitive code
  • Human review of every AI-generated change
  • Input filters that keep secrets and confidential data out of AI tools
  • Automated tests and security checks on AI output

AI-augmented development speeds up work by taking routine tasks off engineers’ plates and shortening feedback loops across the software development life cycle. Here’s how it boosts productivity:

  • Instant scaffolding of code, tests, and prototypes
  • Faster debugging with AI-suggested fixes
  • Automated documentation and changelogs
  • Early detection of issues during code review
  • More engineering time for high-value tasks

However, these gains depend on how AI is used. An experienced AI-augmented software development company combines AI tools with senior engineering oversight to improve delivery speed without compromising quality. Without proper practices and controls, faster coding can lead to more rework and technical debt.

AI-augmented software engineering relies on a combination of tools that support different stages of the software lifecycle. The most common categories include:

  • AI assistants for code generation: Claude Code, OpenAI Codex, GitHub Copilot, Cursor
  • AI code review and testing: Qodo, CodeRabbit, SonarQube, Snyk Code
  • Conversational AI models: ChatGPT, Gemini
  • DevOps and MLOps: Kubernetes, Terraform, Prometheus, Grafana

The right toolset depends on your project, tech stack, and security requirements, so experienced teams select and configure tools for each case.

Contact us

Tell your idea, request a quote or ask us a question
We take ownership to make it a success — because we build what we’re proud of.
Yuriy Kapkovskyy linkedin
Chief Sales and Marketing Officer, Co-owner
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