Summary

• Nettle has raised a $4.8 million seed round led by MTech Capital, taking total funding to $6.8 million.
• Its platform combines risk identification, evidence capture, analysis and reporting for commercial insurers.
• The software is designed to increase inspection capacity while leaving specialist judgement with risk engineers.

London insurance technology company Nettle has raised $4.8 million to expand software designed to help commercial insurers assess more physical risks as experienced risk engineers become harder to replace.

MTech Capital led the seed round, with Project A, Sure Valley Ventures, Portfolio Ventures and Ventures Together participating. The financing takes Nettle’s total funding to $6.8 million following its 2025 pre-seed round and will support further expansion across Europe and the United States.

Loss control teams inspect commercial properties and operations to identify hazards before or during an insurance policy. Engineers may examine fire protection, building conditions, working practices and other factors before producing recommendations that influence underwriting and risk management.

Nettle says some insurers face inspection backlogs of as much as six months while an estimated 40% of risk engineers could retire by 2030. Its platform addresses that capacity problem by automating more of the evidence handling and reporting around specialists rather than attempting to remove specialist judgement from the process.

More of the inspection becomes structured

Risk surveys can produce photographs, video, audio, documents and written observations that later have to be organised into something an underwriter can interpret. Nettle brings those materials into one workspace, applying AI to identify relevant evidence, assist analysis and prepare reports.

Potential risks can also be surfaced before an inspection using available external information, while guided workflows help engineers, agents or policyholders collect evidence in a more consistent form. Human reviewers can then examine the supporting material before accepting or changing the resulting assessment.

That division matters because a recommendation about fire protection or workplace risk carries consequences beyond administrative efficiency. AI can reduce the work required to structure evidence, but the insurer still needs accountable professionals to decide whether a finding is correct and what action it warrants.

Nettle reports that customers can complete assessments as much as five times faster. Allianz Türkiye provides a narrower example: Nettle says its risk engineering team completed property inspections two to three times faster during a pilot before moving to a wider deployment.

Those remain company and customer reported outcomes rather than independent market benchmarks. They nevertheless show where the time savings are expected to come from: less manual evidence processing and report production rather than reducing the physical risk itself.

Automation meets a demographic constraint

The ageing risk engineering workforce gives the technology a different rationale from many AI productivity tools. Insurers may not be able to replace experienced specialists quickly enough to maintain the same inspection coverage using existing processes.

Allowing agents or policyholders to collect structured evidence can extend the reach of a central risk team, while scarce engineers concentrate on complex properties or cases where professional judgement adds the most value. The model depends on being clear about which work can be delegated safely.

Historical reports can also become more useful when their contents are structured rather than stored as isolated documents. Previous recommendations, recurring hazards and evidence from earlier visits can inform the next assessment and help newer staff understand how a risk has changed over time.

Nettle is expanding beyond commercial property into areas including liability, construction and workers’ compensation. Each line brings different evidence and expertise requirements, making the quality of the underlying workflow more important as the platform broadens.

Data governance also becomes significant. Commercial inspections can reveal detailed information about buildings, processes and protective systems. Nettle offers cloud and on premises deployment options and says customer information is not used to train public or shared AI models.

The company works with insurers including Allianz and Brotherhood Mutual and has deployments across several international markets. New funding will allow it to expand sales and engineering while extending the range of risks its system can support.

The formal 6 October funding announcement identifies MTech Capital as the lead investor. Other company material published around recruitment contains a different description of the earlier financing, so the current funding release is the authoritative basis for the investment details used here.

Nettle’s growth therefore depends on more than whether an AI system can draft a risk report. Insurers need to know that evidence remains traceable, professional judgement can override automated findings and sensitive operational information is handled appropriately.

If those controls hold, automation can make a scarce workforce responsible for a larger portfolio without pretending that expertise itself has been automated away. Nettle’s $4.8 million round is built around that narrower proposition: use software to remove more of the administrative work surrounding risk engineers so the remaining specialists can spend more time applying the judgement insurers still need.