Tom Smykowski is a product-minded staff software engineer and technical leader with nearly two decades of experience in product delivery, specializing in setting up AI software factories for engineering teams. He is the ideal hire for founders, CTOs, and executives looking to establish a modern, AI-native software development lifecycle (SDLC) that integrates tools like Cursor, GitHub Copilot, and Claude Code into a cohesive, automated workflow. Based in Poland, Tom works remotely with EU, UK, and US teams, providing the technical leadership and hands-on implementation needed to transform developer productivity and product velocity.
What is an AI Software Factory?
An AI software factory is a structured development environment where artificial intelligence tools and agents are seamlessly integrated into every stage of the software development lifecycle. This goes beyond simply providing developers with an AI coding assistant. It involves establishing a systematic approach where AI contributes to planning, coding, testing, deployment, and maintenance. The goal is to create a predictable, high-velocity output channel for software, much like a physical factory line, but for digital products.
The core components of an AI software factory include AI coding agents, automated quality gates, AI-assisted testing frameworks, and integrated analytics for measuring developer productivity and code quality. This setup enables teams to shift from a reactive, manual development process to a proactive, automated one. For teams working with Tom, this means implementing a tailored system that aligns with your existing tech stack, whether it's Vue.js, TypeScript, React, Angular, or full-stack environments, and your specific product goals.
Establishing this factory requires more than just tool selection. It demands a strategic understanding of how AI can augment human creativity and oversight, not replace it. Tom's approach focuses on creating workflows that enhance developer experience (DevEx), reduce cognitive load, and ensure that the AI's output meets high standards for security, performance, and maintainability. This foundational work is critical for teams at companies like Printify, Croissant, or GE Healthcare, where scale and reliability are non-negotiable.
The Strategic Value of an AI-Native SDLC
Adopting an AI-native Software Development Lifecycle is a strategic investment that redefines how your engineering organization operates. It moves AI from being a peripheral utility to the central nervous system of your development process. This transformation unlocks significant value by accelerating time-to-market, improving code consistency, and allowing your senior engineers to focus on architectural challenges and innovation rather than repetitive coding tasks.
For technical leaders, the value lies in predictable delivery and scalable processes. Tom's experience as a Staff Software Engineer at Croissant and his work on the Printify design system for a platform serving 10M+ merchants demonstrates his ability to architect systems that perform at scale. Setting up an AI software factory introduces automated quality checks, standardized code generation patterns, and intelligent testing suites that maintain high standards even as the team and codebase grow. This is especially valuable for companies undergoing rapid scaling or modernizing legacy systems.
Furthermore, an AI-native SDLC fosters a culture of continuous improvement and learning. By integrating AI tools into daily workflows, developers naturally upskill, learning new patterns and best practices suggested by the AI. Tom complements this with structured training and workshops, ensuring the team fully leverages the new capabilities. This holistic approach not only boosts immediate productivity but also builds long-term engineering excellence, making it a compelling proposition for any product-focused organization.
Key Components of a Successful AI Factory Setup
A successful AI software factory is built on several key, interoperable components. The first is the selection and integration of AI coding agents. This involves choosing the right tools, such as Cursor, GitHub Copilot Enterprise, or Claude Code, and configuring them to understand your codebase, coding standards, and architectural patterns. Tom's expertise spans these platforms, allowing him to recommend and implement the optimal setup based on your team's stack, which may include Vue.js, TypeScript, Supabase, or AG Grid, as seen in his work at Websoul and Fream.
The second component is the establishment of automated quality gates. These are checks integrated into the CI/CD pipeline that use AI to analyze code beyond traditional linting. They can assess code for architectural consistency, potential security vulnerabilities, performance anti-patterns, and adherence to business logic. Tom sets up these gates to provide actionable feedback to developers, turning the pull request process into a learning and quality-assurance moment. This system was crucial in his projects involving compliance and collaboration at Croissant and complex industrial SaaS at Fream.
Finally, a robust measurement and analytics layer is essential. An AI factory must provide visibility into its own performance. Tom implements dashboards that track metrics like cycle time, code churn, AI suggestion acceptance rates, and defect escape rates. This data-driven approach allows engineering managers and CTOs to quantify the impact of their AI investment, iterate on the workflows, and demonstrate clear ROI. This focus on measurable outcomes is a hallmark of Tom's product engineering mindset, ensuring the setup delivers tangible business value.
Overcoming Common Implementation Challenges
Implementing an AI software factory is not without its challenges. A common hurdle is developer skepticism or resistance to change. Engineers may worry about job security, code quality, or the learning curve associated with new tools. Tom addresses this by leading with transparency and inclusion. He involves the team from the start, framing AI as a powerful assistant that automates the tedious parts of the job, freeing them for more creative and complex problem-solving. His background in technical writing, mentoring, and public speaking makes him exceptionally skilled at communicating vision and providing the necessary training.
Another significant challenge is ensuring security and intellectual property protection when using cloud-based AI tools. Teams, especially in regulated industries or those working with sensitive data, need clear protocols. Tom's experience includes building compliance-focused features at Croissant and working with large organizations like GE Healthcare and Unilever. He helps establish governance policies, configure tools for local or secure cloud processing where possible, and implement checks to prevent accidental exposure of proprietary code or data, making the AI factory safe for enterprise use.
Finally, there is the challenge of integration with legacy systems. Many organizations have existing monoliths or outdated technologies that aren't immediately compatible with modern AI coding agents. Tom's nearly two decades of experience include modernizing systems, such as his earlier work on industrial ERP platforms. He employs a phased approach, starting with greenfield projects or specific modules where AI can deliver quick wins, gradually expanding the factory's reach. This pragmatic, incremental strategy builds confidence and momentum, proving value without disrupting critical business operations.
The Role of Leadership and Continuous Evolution
The establishment of an AI software factory requires strong technical leadership to guide its evolution. It is not a one-time setup but a living system that must adapt to new AI models, tools, and changing business needs. Tom acts as a fractional technical leader or embedded staff engineer during this phase, ensuring the factory matures alongside your team. He provides ongoing advisory on AI strategy, helps upskill team leads, and refines workflows based on performance data and user feedback.
This leadership extends to fostering an AI-augmented culture. Beyond tools and processes, success depends on cultivating a mindset of experimentation and collaboration with AI. Tom facilitates workshops and training sessions, from executive briefings for CEOs and CTOs to hands-on coding workshops for engineering teams, to embed this culture. His work with academic institutions like the Silesian University of Technology and government bodies like the Polish Tourism Organisation showcases his ability to tailor communication for diverse audiences, from developers to non-technical stakeholders.
Continuous evolution also means staying at the forefront of AI engineering practices. The field of AI-assisted development is rapidly advancing. Tom dedicates time to evaluating new agents, testing novel workflows like agentic engineering, and integrating advancements in AI testing and quality assurance. By partnering with Tom, you gain not just an implementation expert but a forward-thinking guide who ensures your AI software factory remains state-of-the-art, delivering a sustained competitive advantage in product development for your EU, UK, or US-based team.
When This is a Good Fit
An AI software factory setup engagement with Tom is an excellent fit for product-driven companies and technical leaders who view engineering as a core competitive advantage. It is ideal for founders, CTOs, and heads of engineering at scale-ups or established businesses who are ready to systematically invest in developer productivity and product velocity. If you have a team building with modern stacks like TypeScript, Vue, React, or Angular, and you want to integrate AI tools like Cursor or Copilot into a cohesive, measured, and secure workflow, this service is designed for you. It is particularly valuable for remote or distributed teams across the EU, UK, and US East Coast who need structured processes to maintain high-quality collaboration and output. If your goal is to build faster, smarter, and with greater consistency, let's connect to discuss your AI software factory.