The London-based enterprise software developer has raised funding to scale its agentic artificial intelligence capabilities.
"Ageiro is built for creators, founders, software engineers and enterprises that can no longer afford the cost, friction and slow pace of the conventional software development lifecycle. "Humanity’s last app" represents a fundamental shift in how we view software development. It is one platform that can build and evolve the applications an enterprise needs in days rather than months, while humans retain governance, transparency and control. This is enterprise autonomy with enterprise discipline."
Panghea Capital Partners led the financing round alongside Consello, Blizzard the Avalanche Fund, and Altea Partners.
"We backed TomNext because the industry is long overdue for an LP-first platform. Wealth managers and family offices are drowning in documents, data gaps and operational risk. TomNext’s vision in both its AI approach and its tokenized transaction-layer vision has the potential to become critical infrastructure for alternative-asset allocators globally."
MTech Capital led the seed round with participation from Boost Capital Partners, D2 Fund, and Portfolio Ventures.
"In regulated industries, quality and scale have always been at odds. You can review everything and go broke, or cut corners and hope for the best. AI that actually works changes that equation by handling routine validation at scale while humans focus on the complex cases that need expert judgement."
The pre-seed round was led by NAP with participation from high-profile angel investors to support product and US expansion.
"Consulting workflows are uniquely complex and difficult to build products around. It’s not surprising the space hasn’t changed yet. This will change though, and there’s no doubt that the industry is going to look completely different in 5 years’ time."
Surface Ventures led the pre-seed round to support product development and team expansion.
"The assumption that bigger is always better needs to go. Specialized SLMs routinely outperform frontier models at role-specific tasks—with greater efficiency and reliability. We’re building the infrastructure and developer tooling to bring those benefits to the enterprise."