Barcelona’s deep tech startups in the spotlight
A closer look at the startups competing, the problems they’re tackling, and the ones that catch our attention.
There is a question we’re taking into Barcelona Deep Tech Summit this week:
what will it take for the technologies on show
to become part of everyday life?
For the founders building them, the answer could involve a manufacturing milestone, a clinical study, a first industrial deployment or a customer willing to change how they work. Each demands something different from the team.
That is what makes the start-up line-up interesting to us at Deep Tech Recruitment. We’re going to discover the next generation of deep tech businesses and meet the people working through those decisions.
Ahead of the Summit, we’ve picked five companies we want to understand better.
Ikusa One: mobile security with privacy built in
Our phones carry sensitive conversations, financial information and access to the systems we use for work. Protecting them should not create another place where that information can be exposed.
Ikusa Cybersecurity’s product, Ikusa One, is designed to detect mobile threats such as malware, phishing and malicious network connections. Its defining approach is to inspect traffic on the device, rather than route it through the company’s cloud servers.
That design choice catches our attention. It brings together security engineering, mobile development and the challenge of making protection understandable to the person using it.
We’re keen to hear how the team balances those demands as it develops the product, particularly when technical decisions affect both privacy and ease of use.
Our question: how do you make sophisticated mobile protection simple enough for people to use every day, while keeping privacy central to the design?
Check them out: Ikusa One.
Galtea: helping teams know whether their AI is ready
An AI system can produce an impressive demonstration while still leaving a business with difficult questions about reliability.
Galtea develops tools for testing, evaluating and monitoring AI systems. Its platform generates test cases from requirements and domain knowledge, simulates user interactions and tracks behaviour in production, with a particular focus on regulated industries.
We’re curious about the collaboration behind that work. Deciding whether an AI system is good enough requires agreement on what “good enough” means in the setting where it will be used. That is a conversation between technical teams and the people who understand the consequences of a mistake.
Our question: how do customers translate their domain expertise into meaningful tests, and where is that translation still difficult?
Check them out: Galtea.
SuperQ Quantum: connecting quantum capabilities to business problems
For a business exploring quantum computing, one of the first questions is where it could make a practical difference, and how it fits alongside the computing resources already available.
SuperQ Quantum is building a platform that brings together quantum and high-performance computing. Its products include tools for mathematical optimisation and post-quantum cybersecurity, with applications across areas such as logistics, finance and healthcare.
What interests us is the work involved in translating a customer’s operational problem into something that the right combination of algorithms and infrastructure can address.
That creates an interesting team challenge: how do quantum specialists, software engineers and people with industry knowledge work together to build a useful product? We’re keen to understand which connections the team considers most important.
Our question: when you move from a customer’s business problem to a working solution, where does the team most need expertise that crosses disciplines?
Source: SuperQ Quantum.
NanoChronia: making industrial gases visible
An industrial gas leak is a very concrete problem for sensing technology to solve.
NanoChronia’s Summit profile describes a platform combining nanosensors and machine learning to detect and locate leaks and predict the behaviour of gases including ammonia, hydrogen, methane and hydrogen sulphide.
What interests us is the setting in which that technology has to work. A useful signal must reach someone who can act on it, within the practical constraints of an industrial site.
For us, this raises questions about how sensor development, data interpretation and customer deployment inform one another. We’d like to hear where the team has had to bring those disciplines closest together.
Our question: what does an industrial customer need to see before trusting a new monitoring system in daily operations?
Check them our: NanoChrania
Flomics: bringing biology and computation together
Flomics is developing blood-based diagnostics using cell-free RNA, sequencing and machine learning. Its work explores how patterns in circulating RNA can help identify disease.
It catches our attention because the scientific and computational work are closely connected. How a sample is handled affects the data available; how those data are analysed affects what can meaningfully be learned from them.
We’re keen to understand how the company organises that collaboration, and what its team is learning as it works towards clinical applications.
Our question: where do experimental biology and computational analysis most need to challenge each other to move the product forward?
Check them our: Flomics.
These companies work on very different problems. The common thread in our watchlist is the point where specialist knowledge has to become a product someone can use and trust.
That is also where we find the most interesting team questions. Which capability should stay close to the founding team? When does a new discipline need its own leader? What experience will help the business reach its next milestone?
We’ll be listening for those answers across the pitches, demonstrations and conversations at the Summit. We expect to come away with a longer watchlist.
If you’re a founder, we’d love to hear which milestone comes next for your business, and what the team needs to get there.
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