Who is Tom Smykowski and Why Hire Him for AI Integration?
Tom Smykowski is a product-minded senior software engineer with nearly two decades of experience in product delivery, specializing in integrating artificial intelligence into existing software products. He is the ideal hire for founders, CTOs, and executives who need to modernize their platform with AI capabilities, from strategic planning through to production deployment. His approach combines hands-on engineering with deep product thinking, ensuring that AI integrations solve real user problems and drive business value, not just technical novelty. Based in Poland, Tom works remotely with teams across the EU, UK, and US, offering the flexibility and time-zone overlap needed for seamless international collaboration.
Tom's career is defined by taking ownership of complex product challenges from discovery to launch. He has served as a Staff Software Engineer at Croissant, a workplace platform, where he partnered closely with the CEO and CTO on compliance and collaboration features. At Printify, he led the design system and rebrand initiative ahead of a major merger, impacting a platform used by over 10 million merchants and a frontend engineering team of around 60. As the founder of Websoul, he built a marketing automation SaaS serving over 32,000 businesses, utilizing modern stacks like Vue.js, TypeScript, and Supabase. This background in scaling products, managing complex systems, and leading engineering efforts provides the perfect foundation for integrating AI thoughtfully and effectively into your existing codebase.
Hiring Tom for AI integration means engaging a professional who understands that technology must serve the product vision. He focuses on creating maintainable, scalable, and user-focused AI features, whether that involves adding intelligent automation, building agentic workflows, or implementing large language model (LLM) interfaces. His work with clients like GE Healthcare, Unilever, and Bunge demonstrates his ability to navigate enterprise environments and deliver robust solutions. For teams looking to enhance their product with AI without disrupting core functionality or developer velocity, Tom offers the experience and strategic insight to make the integration a success.
The Strategic Approach to AI Product Integration
Integrating AI into an existing product is not merely a technical task, it is a product strategy exercise. The first step is always discovery: understanding the user's pain points, the business objectives, and the existing architecture's constraints. Tom employs a product-thinking methodology to identify where AI can deliver the highest leverage. This could be automating repetitive tasks, providing predictive insights, personalizing user experiences, or creating entirely new interactive capabilities. The goal is to augment the product's core value proposition, not to add AI for its own sake.
This strategic phase involves close collaboration with product managers, designers, and key stakeholders. Tom facilitates workshops and technical discussions to map out user journeys and identify integration touchpoints. He assesses the current tech stack, data availability, and performance requirements to recommend the most suitable AI approaches, whether using off-the-shelf APIs, fine-tuning open-source models, or building custom machine learning pipelines. His experience across industries, from fintech SaaS at Websoul to industrial mapping at Fream, gives him a broad perspective on how different domains can leverage AI effectively.
The outcome of this strategic work is a clear roadmap. This roadmap prioritizes AI features based on impact and feasibility, defines success metrics, and outlines the technical architecture. It ensures that the engineering team has a coherent plan that aligns with business goals and user needs. For leadership, this provides confidence that the investment in AI is directed and measurable. Tom's ability to communicate this strategy clearly, from executive briefings to detailed technical specs, ensures everyone from the CEO to the development team is aligned from the start.
Technical Execution: From Architecture to Production
With a strategy in place, the focus shifts to technical execution. Tom's full-stack engineering expertise is crucial here. He architects AI integrations that are secure, scalable, and maintainable within your existing product ecosystem. This involves designing APIs, data flow, model serving infrastructure, and user interface components. He is proficient with modern JavaScript frameworks like Vue.js and React, TypeScript for type-safe development, and backend technologies like Supabase and Node.js, making him adaptable to most tech stacks.
A critical consideration is how the AI integration affects the existing codebase and developer experience. Tom emphasizes creating clean abstractions and well-documented interfaces. This might involve building internal SDKs or design system components for AI features, ensuring consistency and reusability. For example, his work on the Printify design system for a large engineering team directly translates to creating scalable UI patterns for AI-powered interfaces. He also focuses on non-functional requirements: latency, error handling, cost management of AI APIs, and observability. Integrating monitoring and logging from day one is essential for maintaining reliability in production.
The development process itself can be supercharged with AI-native practices. Tom advocates for and implements AI-assisted software development lifecycles (SDLC). This includes using tools like GitHub Copilot, Cursor, or Claude Code to improve developer productivity, writing AI-augmented tests, and establishing quality gates with automated code reviews. Setting up an "AI Software Factory" environment helps the entire team build and iterate on AI features faster. His consulting offerings in this area mean he can not only build the integration but also uplift the team's capabilities for future AI work, leaving a lasting positive impact on your engineering culture.
Overcoming Common AI Integration Challenges
Every integration project faces hurdles. A common challenge is data: its quality, availability, and format. Existing products may not have data structured in a way that's ready for AI. Tom's experience includes building data pipelines and working with diverse data sources, from AG Grid tables in enterprise applications to real-time sensor data in industrial SaaS. He can design the data preprocessing and feature engineering steps needed to feed AI models effectively, often starting with a pragmatic MVP that uses available data before investing in large-scale data infrastructure.
Another significant challenge is managing the cost and performance of AI models, especially LLMs. Tom employs techniques like prompt engineering, caching, model quantization, and fallback strategies to optimize for both user experience and operational expense. He helps teams choose between different model providers and deployment options (cloud vs. on-premise, API vs. self-hosted) based on the specific needs of the product. His work with compliance-sensitive sectors like healthcare (GE Healthcare) and finance-adjacent platforms (Websoul) also informs his approach to security, data privacy, and ethical AI use, ensuring integrations adhere to relevant regulations.
Finally, user adoption is a make-or-break factor. The most sophisticated AI feature is useless if users don't understand or trust it. Tom's strong UX sensibility, honed through projects focused on conversion-focused design, ensures AI features are intuitive and provide clear value. He advocates for iterative deployment, using A/B testing and user feedback to refine AI interactions. This human-centered approach mitigates the risk of building a technically impressive but ultimately unused feature, ensuring the AI integration genuinely enhances the product.
Proven Impact Across Industries and Platforms
Tom's portfolio demonstrates the tangible impact of his engineering work across different sectors. At Croissant, a collaboration platform, his contributions to compliance and team features required a deep understanding of user workflows and system reliability, skills directly transferable to building trustworthy AI agents. The scale of his work at Printify, affecting millions of merchants and dozens of engineers, proves his ability to operate and make changes in large, complex codebases, a must-have for integrating AI into established products.
His founder experience with Websoul is particularly relevant. Building a marketing automation SaaS from scratch involved creating intelligent, rule-based systems that anticipate user needs, a precursor to modern AI-driven automation. Scaling this to over 32,000 businesses required robust architecture and a focus on performance, lessons he applies to AI integrations that must handle variable load. Furthermore, his engagements with enterprise clients like DeLaval, Ordnance Survey, and Unilever show he can navigate large organizations, understand domain-specific problems, and deliver solutions that meet stringent corporate standards.
This diverse background means Tom doesn't just bring generic AI knowledge, he brings context. He understands the unique constraints and opportunities in B2B SaaS, enterprise software, industrial applications, and startup environments. Whether you're modernizing a legacy ERP system, adding smart features to a geospatial platform, or building AI into a consumer-facing app, he has the breadth of experience to identify patterns, anticipate pitfalls, and deliver a robust integration. His remote collaboration with US, UK, and EU teams on these projects ensures he can slot into your workflow seamlessly.
When Hiring Tom for AI Integration is a Good Fit
This engagement is an excellent fit if you are a founder, CTO, or product leader with an existing software product that needs a strategic, hands-on engineer to own the AI integration from concept to production. It is ideal if you value product thinking as much as technical execution and need someone who can bridge the gap between business strategy and engineering implementation. You should be looking for a staff or senior-level contributor who can provide technical leadership, mentor team members, and set best practices for AI development within your organization.
It is a strong match if your team is based in the EU, UK, or US (East Coast) and operates remotely or in a hybrid model. Tom's location in Poland provides convenient time-zone overlap for synchronous collaboration when needed, supported by strong async communication practices. The fit is best for projects where the goal is to meaningfully enhance an existing product with AI features, agents, or workflows, rather than building a standalone AI prototype from zero. If you are ready to invest in a high-quality, sustainable integration that delivers user value and positions your product for the future, then let's start a conversation.