Matthew Fitzpatrick, CEO of Invisible Technologies – Interview Series

Matthew Fitzpatrick is an experienced operation and growth specialist with deep experience in expanding the workflow and complex teams. Thanks to a background that extends to consulting, strategy and operational leadership, it is currently working as an executive head of Invisible Technologies, as it focuses on designing and improving comprehensive business solutions. Matthew is excited to combine human talent and automation to pay efficiency on a large scale, which helps companies open the transformative growth by creating the process.

Invisible Technologies is a commercial automation company that blends advanced technology with human experience to help institutions expand their scope efficiently. Instead of replacing human beings with automation, Invisible creates a dedicated workflow as digital workers (software) and human operators cooperate smoothly. The company provides services through areas such as enriching data, generating customers, customer support, and back office operations-which gives customers to delegate complex tasks, repetition and focus on basic strategic goals. The unique Invisible model provides “Work as a service” for institutions that are evaluated, transparent and effective in terms of cost.

I recently moved from Leadumblack Labs in McKINSEY to become the CEO of Invisible Technologies. What attracted you to this role, and what excites you more about the Invisible mission?

In MCKINSEY, I had the honor to work at the forefront of AI Innovation – building artificial intelligence software products, leading research and development efforts, and helping institutions to harness the strength of data. What attracted me to invisible technologies is the opportunity to make them work widely through a group of uniquely elastic artificial intelligence software platform and expert market to feed the human act in the episode-I believe that learning reinforcement from human comments (RLHF) is the key and reliable Genai applications. Invisible supports artificial intelligence via the entire value chain, starting with data cleaning and automating data entry to thinking about the designated chain and thought of assessments. Our mission is simple: combining human intelligence and AI to help companies meet the potential of artificial intelligence, which in the institution was much more difficult than most people expected.

He supervised more than 1,000 engineers and expanded the range of Amnesty International products through industries. What are the lessons of MCKINSEY you are submitting to the next stage of growth in Invisible?

Drawn Yabrezan. First, successful artificial intelligence revolves around organizational transformation as much as technology. You need the right people and existing operations – as well as wonderful models. Second, it is companies that win artificial intelligence that mastered the “last mile” – the transition from experimentation to production. In Invisible, we apply the same rigor and structure to help customers overcome pilots and production, which provides real commercial value.

I said that “2024 was the year of experimenting with artificial intelligence, and 2025 revolves around achieving the return on investment.” What are the specific trends that you see among the institutions that already achieve this return on investment?

Companies that see a real investment revenue this year do three things well. First, it is compatible with cases of artificial intelligence tightly with the main performance indicators of basic business – such as operational efficiency or customer satisfaction. Second, they invest in better quality data and human comments rings to constantly improve the performance of the model. Third, they move from public solutions to systems specifically designed for the field that reflect the complexity of their environments. These companies are no longer just a artificial intelligence test – they expand their scope with the purpose.

How is the demand for data for the field and the PhD levels through the baseline models such as AWS, Microsoft and Cohere?

We see an increase in demand for specialized signs as the foundation model provides to more complex arches. In Invisible, we have a 1 % annual acceptance rate on our experts group, and 30 % of our trainers keep a master’s or doctorate. This deep experience is increasingly necessary-not only for the accurate conditions on the data, but to provide accurate and aware of the context to improve thinking, accuracy and alignment. With the growing smartest, its training tape rises.

Informed at the forefront of Agency AI, focusing on making decisions in the workflow in the real world. What is your definition of artificial intelligence agent, and where do we see the most promise?

Agency -AI refers to systems that only respond to instructions – they are planning, make decisions, and make action within the specified handrails. It is artificial intelligence that behaves like a teammate more than one tool. We see the largest traction in the complex workflow of large size: such as customer support and insurance demands, for example. In these areas, artificial intelligence agent can reduce manual voltage, increase consistency, and provide results that otherwise require a large human difference. It is not about replacing humans – instead, we increase them with smart factors that can deal with repetition and routine.

Can you share examples of how invisible trains for thinking about the thinking chain and why is it important to publish institutions?

The idea of ​​the idea series (COT) has opened new capabilities for the AI. In Invisible, we train models to cause step-by-step, which is necessary when the risks are high-whether you diagnose a patient or a contract analysis or verify the validity of a financial model. Not only improve transparency, it also enables the gains of errors, improvements and performance without new new data groups. We have seen pioneering models such as Gemini, Sonnet and Grok that start to detect their thinking paths, which allow us to monitor not only the resulting models, but how to get there. This sets the basis for more advanced ways such as the thought tree (where models evaluate potential multiple thinking paths before settling an answer) and self -dates (where multiple thinking paths are explored).

Invisible Supports Training across 40+ coding languages ​​and 30+ human languages. How important is cultural and linguistic accuracy in building artificial intelligence worldwide?

It is very important. Language is not only about translation – it relates to context, differences and cultural standards. If the abuse of the model is misrepresented or the regional contrast is lost, this may lead to poor user experiences, or even the risk of compliance. Our multi -language coach are not just fluent – they are included in the cultures they represent.

What are the common failure points when companies try to expand the concept to production, and how does invisible help in this “last mile”?

The majority of artificial intelligence models do not reach production because companies reduce the required operational elevator. They lack clean data, strong evaluation protocols, and a strategy to include models in real workflow tasks. In Invisible, we combine deep technical experience with data infrastructure to help institutions bridge the gap. Our symbiotic capabilities in training and improvement allow us to build better models and spread them successfully.

Can you walk through the Invisible approach in RLHF (learning reinforcement from human reactions) and how it differs from others in the industry?

In Invisible, we see learning reinforcement from human comments (RLHF) as more than just good control – it allows a more dedicated dedicated assessment design (“” EVAL “), and a shift towards training models with careful human rule instead of bilateral signals such as thumb up and thumb. While industry approaches often give priority to the scale through high -sized data, we focus on collecting high -quality organized reactions that embody thinking, context and bodies. This richer sign provides models of generalization more effectively and more closely compatible with human intent. By giving priority to depth on a breadth, we build the infrastructure of the most powerful and aligned artificial intelligence systems.

How do you imagine the future of AI-Human’s cooperation, which develops, especially in the areas of high risk such as financing, health care, or the public sector?

Artificial intelligence does not replace human experience – the infrastructure it supports has become. I imagine a future in which artificial intelligence agents and human experts work alongside – where doctors are supported by diagnostic continuous, government agencies use Amnesty International for the benefits of sorting more efficiently, and financial analysts are free to focus on the strategy instead of data schedules. Our focus is on the design of systems where artificial intelligence enhances human ability, rather than blocking or canceling it.

Thank you for the wonderful interview, readers who want to know more have to visit invisible technologies.