Matthew Fitzpatrick is an experienced specialist in operations and growth with deep expertise in scaling of complex workflows and teams. Thanks to the background, which includes advice, strategy and operating management, it currently serves as the CEO of Invisible Technologies, where it focuses on designing and optimizing business solutions end-to-end. Matthew is a passionate combination of human talent with automation to control scale efficiency, helping companies unlock transformation growth through innovation processes.
Invisible Technologies is a company for automation of business processes that mixes advanced technologies with human expertise that helps organizations effectively expand. Rather than replacing people by automation, Invisible creates their own workflows where digital workers (software) and human operators work smoothly. The company offers services across areas such as data enrichment, lead generation, customer support and advance operations that enably customers to delegate complex, repeated tasks and focus on basic strategic goals. The unique model “Work-AA-Service” Invisible provides companies scalable, transparent and cost-effective operational support.
You have recently switched from Quantumlack’s leading laboratories to McKinsey to Invisible Technologies. What attracted you to this role and what excites you with the most Invisible mission?
In McKinsey, I had the honor of working in the foreground of AI Innovation – building AI software products, leadership of research and development and help to use the power of data. What attracted me to invisible technologies was the opportunity to be functional in the range with a combination of a uniquely flexible AI software platform and a professional market for human feedback-in-the-i VIERIF strengthening from human feedback (RLHF). Invisible supports AI throughout the value chain, from data cleaning and automation of data entry to chain reasoning and customs. Our mission is simple: combine human intelligence and AI to help businesses do the potential of A’A, which was much harder in the company than most people expected.
You have supervised 1,000+ engineers and have reduced more AI products across industries. What lessons from McKinsey do you relate to the next phase of Invisible growth?
Two lessons stand out. First, successful adoption AI is as well as organizational transformation as technology. You need the right people and processes – on top of great models. Second, companies that win are those who control the “last mile” – the transition from experience to production. At Invisible, we apply this strictness and structure that helps customers to move to pilots and production and provide actual business value.
You said “2024 was a year of experimenting AI and 2025 is about the realization of the king.” What specific trends do you see among businesses that really achieve the king?
The businesses they see this year do three things well. First, it firmly aligns the use of AI boxes with the main commercial KPI – such as operating efficiency or customer satisfaction. Secondly, they invest in quality data and loops for feedback to human feedback to constantly improve the performance of the model. Thirdly, they move from general solutions to a customized domain security system that reflects the complexity of their surroundings. These companies have long been testing AI – they scalp it with purposes.
How does a domain security application and doctoral data labeling with the Foundation’s providers such as AWS, Microsoft and Coere?
We see that we see an increase in demand for specialized marking, as the providers of the foundation model are pushing into more complex verticals. In Invisible, we have 1% of the annual acceptance rate on our professional fund and 30% of our coaches hold a master’s or doctoral students. This deep expertise is increasingly needed, not only for accurate data annotations, but to provide a-wi-wafa feedback on improving reasoning, accuracy and reconciliation. Once the models are smarter, the bar for their training increases.
Invisible is at the forefront of agent artificial intelligence and emphasizes decision -making in the real world. What is your definition of AI AI and where do we see the most promised?
Ai AI refers to systems that only respond to instructions – plan, decide and act in defined railings. It is an AI that behaves more like a teammate than an instrument. We see the most traction in high -volume, complex workflows: for example, customer support and insurance requirements. In these areas, AI AI can reduce manual efforts, increase consistency and provide results that would otherwise require a large human team. It is not about replacing people – Intear, we have agent with intelligent substances that can handle recurring and routine.
Can you share an example of how Invisible Trains Models to Consider Chain and Why is it important for the company’s deployment?
The chain of thoughtful (cot) justification unlocked the new potential for the corporate AI. We train models in Invisible to consider step by step, which is necessary when high -level bets are diagnosed with a patient, contract analysis, or the financial model verification. COT not only improves transparency, but also tuning, improvement and performance profits without massive new data sets. We have seen leading models such as Gemini, Sonet and Grok will start to publish their justification routes, allowing us not only to observe what models are, but as they come. This is to lay the basis for more advanced methods, such as a tree of thinking (where models evaluate several possible paths of thinking before settling on answers a) and self -construction (where several ways are examined).
Invisible supports training across 40+ coding languages and 30+ human languages. How important is the cultural and language accuracy in building a globally scalable AI?
It’s critical. The language is not just a translation – it is a context, nuances and cultural standards. If the model thinks of a tone or is missing regional variations, this can lead to poor user experience or compliance. Our multilingual coaches are not just run – they are built into the cultures that are.
What are the normal points of failure when companies try to scalance out of the concept after production and how does invisible navigation in this “last mile” help?
Most AI models will never get into production because companies underestimate the required operating elevator. They lack clean data, robust protocol evaluation and strategy for inserting models into real work flows. In Invisible, we combine deep technical experience with data infrastructure for production to help businesses bridge the gap. Our symbiotic skills in training and optimization allow us to create better models, and it is successfully deployed.
Can you go through the Invisible approach to RLHF (strengthening human feedback learning) and how does it differ from others in the field?
In Invisible, we see learning strengthening from human feedback (RLHF) as more than just tuning – allows more sophisticated design of one’s own evaluation (“rating”) and shift towards training models with nuances of human judgment than binary and thumbs down. While industrial approaches often prefer a scale through high volume data, low signal, we focus on the collection of structured, high quality feedback that context and compromises. This richer signal makes it possible to generalize the models more efficiently and to cope more with the human intention. By preferring depth over width, we build infrastructure for more robust and aligned AI systems.
How do you invite the future of cooperation between Ai-Hide developing, especially in high-betting fields such as finance, health care or public sector?
AI does not replace human expertise – it becomes an infrastructure that supports it. I imagine the future where AI agents and human experts work in Tandem – where clinic doctors are supported by diagnostic copilots, government agencies use more efficient advantages and financial analysts can focus more on strategy than a table. Our focus is the design of a system where AI increases human ability rather than covering or overturning.
Thank you for a great interview, readers who want to learn more should visit invisible technologies.
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