Intellegens is a company that helps you apply advanced machine learning to find new product and process solutions, get products to market faster, and break through R&D bottlenecks. Pulse 2.0 interviewed Intellegens CEO Ben Pellegrini to gain a deeper understanding of the company.
Ben Pellegrini’s Background
What is Ben Pellegrini’s background? Pellegrini said:
“I have been working with data for over 20 years, managing it and getting the most value out of it. Originally, I worked at Oracle, supporting a wide range of global enterprises. ‘Pre-Cloud’ data management, security, replication and redundancy looked very different. I worked in highly regulated data environments for clinical and health services, so I understand the importance of truly robust security and of enabling collaboration in large organizations. More recently I have worked in startups and scaleups to CTO level looking after data and software infrastructure.”
Formation Of The Company
How did the idea for the company come together? Pellegrini shared:
“I met Gareth Conduit in 2017 through a joint contact who at the time was working at Cambridge Enterprise – the commercialisation arm of Cambridge University. This contact recognised the synergy between the algorithms that Gareth was developing at the Cavendish Laboratory (the University’s Physics Department) and the experience I could bring on how to deploy technology in wider data-driven software systems.”
Favorite Memory
What has been your favorite memory working for the company so far? Pellegrini reflected:
“Positive customer feedback is always great to hear – deploying new technology in long standing industries is challenging and can take a lot of work to prove the value. A big purchase order is always nice too!”
Core Products
What are the company’s core products and features? Pellegrini explained:
“The core product is the Alchemite Suite. Underlying the Suite is a powerful machine learning algorithm that has evolved from the original Alchemite method developed at Cambridge University. The Suite provides targeted apps, each focused on a key challenge for R&D scientists, experimentalists, data scientists, or managers. They work together to deliver an integrated machine learning solution for an R&D organization. Specific apps include:
Alchemite Viewer which enables managers and other R&D team members to view their team’s Alchemite projects, explore results, and support decision-making. Alchemite Explorer enables scientists to quickly upload data, generate a machine learning model, and immediately apply that model to predict, analyze, and optimize the system they are studying.
Alchemite Designer makes it easy to set up and run Design of Experiments (DOE) projects in just a few button clicks, helping teams to cut experimental workloads by 50-80% compared to conventional DOE.
Alchemite Innovator combines predictive tools with a quick and easy DOE method to provide a comprehensive project toolset.
Alchemite Architect provides advanced API-based access to the Alchemite computational engine, enabling it to be integrated with other R&D IT solutions or used in automation tasks.”
Challenges Faced
Have you faced any challenges in your sector of work recently? Pellegrini acknowledged:
“Things have changed significantly since we started in 2017. Alongside the day to day challenges of running a business we have navigated the impact of COVID and the energy crisis and war in Ukraine which had a big impact on the materials and chemical industry in Europe. More recently the economic environment is still uncertain making it harder for enterprises to invest in new technologies. There has also been the launch of ChatGPT and deepseek – resulting in perhaps, the fastest-growing technology of all time. These new AI tools do different things to Alchemite, but they are increasing the focus on how AI will transform the future of work, and we want to be a key part of that journey. Every challenge is an opportunity!”
Evolution Of The Company’s Technology
How has the company’s technology evolved since launching? Pellegrini noted:
“When we initially launched we really had just an algorithm – we worked closely with our customers, using the technology internally to deliver projects. As we understood the challenges and the need, we developed an early prototype of the software that evolved into a cloud based application.”
“That has developed over the last few years – culminating with the launch of the Alchemite Suite in January 2025 with a very strong focus on user experience, applying the underlying algorithm through task-focused apps. Along the way we have also implemented ISO27001 to give our customers confidence in our security practices.”
“Looking forward, we have three new exciting products to build on the already deployed infrastructure, utilising large language models, federated learning, and deploying on hardware.”
Significant Milestones
What have been some of the company’s most significant milestones? Pellegrini cited:
— 2017 – we won an InnovateUK grant to help launch the company, building an early software demonstrator and enabling us to start talking to customers
— 2018 – we had our first customers and collaborators, and took on our first employees and office space
— 2019 – partnership with Optibrium to embed Alchemite within their Cerella product for drug discovery
— 2020-2024 – established a commercial software product and began to generate success stories with a growing customer base
— 2021 – recognized as Best Software Product for Additive Manufacturing by the American Society of Mechanical Engineers (ASME)
— 2023 – moved to new headquarters at Chesterton Mill in Cambridge
— 2023 – recognized as AI Company of the Year in the Cambridge Independent Science & Technology Awards, competing with a very strong peer-group of scale-up AI companies in the ‘Silicon Fen’ area
— 2025 – launch of Alchemite Suite.
Customer Success Stories
When asking Pellegrini about customer success stories, he highlighted:
“These are some of our published case studies (we also have many projects that remain confidential):
— Johnson Matthey found routes to increase experimental efficiency in two projects developing catalysts significantly
— Domino Printing Sciences cut experimental timescales by months in the reformulation of inks
— Global dairy products leader Yili sped up the development of cream formulations
— Voestalpine saw nearly 40% cost savings in the manufacturing and testing of Additive Manufacturing alloys
— NASA validated Alchemite for the design of alloys and heat exchangers
— CPI is optimizing biopharmaceutical manufacturing processes.
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Pellegrini assessed:
“It would be hard to analyse the market in this way for our technology, and not very meaningful to do so. The total market for software technologies to support R&D and engineering in knowledge-intensive industries (our key focus at present) is many billions of dollars, and our technology has the potential to integrate with, complement, or replace applications right across that spectrum. The best way to look at it is that the market potential is enormous compared to our current scale, and our focus is on finding and serving the applications within that market that can deliver value to our customers and Intellegens as quickly as possible.”
Differentiation From The Competition
What differentiates the company from its competition? Pellegrini affirmed:
“Against applications in areas such as Design of Experiments that are not based on machine learning (ML) – the ability of ML to rapidly find and use relationships in data that other (e.g., statistical) methods do not identify. This both delivers better results and makes it possible to build quick, easy-to-use apps.”
“Against other ML solutions for R&D – the underlying algorithm is good when there is missing data, which is typical of experimental and process datasets and which causes most ML methods to fail. Also, our focus on delivering the technology in targeted apps focused on key R&D tasks, which derives from another key differentiator – we now have extensive experience from hundreds of ML-focused R&D projects.”
Future Company Goals
What are some of the company’s future goals? Pellegrini emphasized:
“As mentioned above, we have some new products in development. Our real strength is in how we can enable our customers to focus ML effectively on high value problems in order to apply it effectively, so our goal is to continue to identify such problems and to develop the right solutions for them. In doing this, we need to balance the very broad potential applicability of the technology against the need for a laser-focus on understanding particular tasks in order to deliver outstanding usability.”
Additional Thoughts
Any other topics you would like to discuss? Pellegrini concluded:
“Just to say the AI technology landscape is moving so fast right now it is really difficult to stay up to date and grounded in reality. Intellegens is built on a foundation of applied ai, in other words, ai that can deliver value. I would welcome any opportunities to discuss how our sector and community can stay informed and make the right decisions to apply these technologies most effectively.”
