Our new Meet the Insurtech Leader series gives you a look into the minds of the executives guiding these tech-forward companies.
Insurtech founders and leadership are encouraged to reach out to potentially be featured in the series:
Who are you, and how did you come into your leadership position?
I am Martin Henley, founder and group chief executive officer of mea Platform.
Before starting the company, I spent more than twenty years in insurance in senior technology roles across the industry, including as group CIO of XL Catlin, now AXA XL. Across that career, I witnessed a long run of operational investment and transformation programs that produced little performance gain. I always believed reinsurance operations could be better, but the technology could never keep up with how complex the work is. AI, trained specifically for insurance, finally changed that: the back office can now be a solved problem that simply runs, rather than an area of spend that never improves performance. Seeing that opportunity, I started Mea in 2021, on the conviction that the answer had to come from people who had sat in the chairs and done the work, not those coming in to "disrupt" the industry. I built the team around exactly those people: operators and technologists drawn from the world's largest reinsurers, brokers and consultancies. We bootstrapped the company for four years, making sure the product had real fit before raising external capital for the next phase of growth.
What advice would you give to other insurtech leaders, or to yourself five years ago?
My advice would be to solve a problem the industry actually has–and prove people will buy the answer, before you fall in love with building it. We did not invest heavily in mea until we were confident on three things: the problem was real, we could build the technology and customers would pay for it.
The second lesson is about people. Build a team that pairs deep insurance experience with serious technology talent, because insurance rewards judgement that takes decades to develop and there is no shortcut to it.
The advice I'd give myself five years ago would be to keep going. This is the first time in my career where the hype for innovation may actually undersell the potential for innovation. Keep pushing and make an impact.
How do you see AI changing insurance over the next five years?
The first wave of AI in insurance was experimentation, with single models bolted onto single tasks. The next five years belong to production. The durable advantage will be the structured insurance knowledge an organization encodes — its own language, risks and decisions — because access to a general model is now universal and so no longer a differentiator on its own. The carriers and brokers who treat their operational knowledge as an asset to be encoded will pull away from those who treat AI as a feature to be bought.
What is the biggest opportunity for insurtechs over the next six months?
Budgets are shifting from experimentation to production. For two years, the industry funded pilots. Now leadership wants results that show up in what they are measured on, and that shift rewards a very different kind of insurtech than the one the market backed last time.
The first wave set out to compete with insurers and to find problems to fix, with a tech solution. It was front-end and consumer-led, built largely by technologists, funded for growth and measured on user numbers. A lot of it ran hot and never reached profitability.
This wave is recognizable by the opposite traits. It is built by insurance people and equips carriers and brokers rather than trying to replace them. It is judged on financial outcomes an insurer can see in its own results, and the strongest companies are capital-efficient and already profitable. The technology is proven at scale today.
The opportunity over the next six months belongs to the insurtechs that clearly sit in this second wave. The market has learnt to tell the two apart, and it is buying accordingly.
What is the biggest mistake insurtechs make these days?
The biggest mistake is promising more than the technology delivers. The industry is tired of demos that dazzle in a thirty-minute meeting and then take years to roll out, cost millions and quietly get abandoned.
The deeper version of this mistake is treating access to a model as if it were a product. The model is the commodity. The product is everything around it — the domain knowledge, workflow, accountability, trust and ability to go live in weeks and deliver from day one. Teams that fall in love with the technology, rather than the operational outcome, tend to stall at the pilot stage.
What is the meaning behind mea Platform's name?
"Mea" is "my" in Latin. We wanted a name where clients could feel individual ownership.
What is your primary line of business?
Mea's primary line of businesses is insurance-specific, AI-native technology that automates reinsurance operations end to end for carriers, brokers and MGAs. Mea is built on a proprietary domain-specific language model and the insurance knowledge graph, so its agentic AI is grounded in the language and logic of insurance and deploys without disrupting existing systems.
What's the origin story of the company?
Mea was founded in 2021 on a simple idea: that reinsurance operations could be better, and moreso, could be solved for good by freeing carriers, brokers and the rest of the industry to focus on what actually creates performance differentiation. For years, outsiders had arrived promising to "disrupt" operations; many took more than they gave back. Mea was built the opposite way, by people who had sat on the other side of the fence, bringing career practitioners together with world-class technology talent. We believed the only way to solve the problem was to build insurance AI from the ground up, not retrofitted from a general-purpose model. So, we built the industry's only domain-specific language model (dsLM), and a proprietary insurance knowledge graph: the foundation that allows our AI agents to operate accurately and auditably at scale on high-stakes insurance work.
When was it founded?
Mea was founded in 2021 with first global deployments in 2022.
What pain points is the technology trying to solve?
Insurance still runs on highly manual and resource-intensive processes, despite years of technology investment. Mea automates these operations with agentic AI, reducing operating costs and lifting underwriting capacity, so expert people can focus on writing the right business rather than processing it.
What funding rounds has the company had?
Intentionally bootstrapped and profitable from inception, in February 2026 Mea secured a $50 million minority growth equity investment from Scottish Equity Partners (SEP) to accelerate product development and customer expansion.
What's ahead?
Our goal is to continue growing mea operations across underwriting, claims, finance and broking, adding capabilities that make our clients' day-to-day work easier and their operations better. Part of that improvement is built into the platform itself: the longer a client runs on mea, the sharper its model becomes through learning their own data, decisions and risk-appetite. Backed by our $50 million growth equity investment from SEP in February 2026, we will accelerate product development and customer expansion.
How many employees does your company have?
We have approximately 130 employees.
Where is the company based?
The company is domiciled in Bermuda, with a global team and global client base spanning live deployments in more than 20 countries.
