Investment ThesisWe invest in the critical-systems layer of deep tech
Transformative software for the systems the world depends on — and the governance, reliability and security layer that the age of AI now demands.
What we look for
- Exceptional, domain-expert founders building at the edge of science and engineering
- Deep-tech software with defensible, proprietary technology
- Large markets where software is the lever
- Cultural fit with the Critical ecosystem
- A problem where getting it wrong is not an option
Our process
Screening
Initial fit against the thesis and the critical-systems lens.
Deep dive
Technical and business diligence — we validate the technology, not just the deck.
Strategic plan
We shape the plan and the round alongside the founders before we commit.
We commit early, write meaningful cheques, and stay close for the whole journey.
Where we invest
We focus on deep-tech software verticals where the Critical ecosystem gives our founders a genuine, unfair advantage.
Cybersecurity
Securing software, infrastructure and now autonomous agents
AI / ML & Decentralised Tech
Governance, reliability and the assurance layer for AI
Defence & Dual-Use
Software for sovereignty, resilience and national security
Medical Software
Where regulation is hard and silent failure is unacceptable
IoT, Automation & Digitalisation
The software layer of modern industry
Mobility & Smart Cities
Autonomy and the systems that move the world
Where we look for differentiation
Below are some of the industries where software quietly runs the systems the world depends on. Getting something wrong here carries a real-world cost, not just a refund. As AI accelerates across every one of them, the bar for reliability only rises, and so does the value of meeting it.
Industries — where failure isn't an option
The moats — why it's defensible
Within those industries we back businesses whose advantage compounds rather than erodes as AI accelerates. A few moats tell us a company is genuinely defensible — and the more of them it holds, the deeper our conviction.
Proprietary data
Unique operational, clinical and sensor data an agentic model can't scrape, synthesise or copy — and that only deepens with every deployment.
Hardware / software co-design
Software co-designed with the hardware it runs on — at the edge, in firmware, on the device — the kind of depth you can't fake with an API call.
Regulatory certification
Safety and compliance certifications — and the years of work behind them — that rivals can't simply step around.
AI control-plane positioning
As agentic AI commoditises the application layer, value moves up into the control plane — human authorisation, output reliability, model assurance and AI-specific security. The same pressure forcing enterprises to deploy AI forces them to govern it.
Building in the critical-systems layer?
If this is your world, we should talk.
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