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Kestrix secures £500,000 to build 3D map of heat loss


Business to business SaaS retrofit management start up Kestrix Ltd has closed a £500,000 pre-seed equity financing round.

The round closed oversubscribed in just under three months, and included participation from Notion Capital, CarbonI3, and angel investors including founders of Vivid Economics (Cameron Hepburn), Kontor (Luke Appleby), Comply Advantage (Charlie Delingpole), and Peakon (Phil Chambers).

The round will support the development of Kestrix’s AI technology, which constructs quantitative 3D heat loss models of buildings from thermal and visible spectrum drone images, informing energy efficiency retrofit planning, pricing, and verification at scale for asset owners, local governments, and energy utilities.

Kestrix co-founder and CEO Lucy Lyons said, ‘The velocity at which industry must decarbonise buildings is daunting, and labour and financial resources are limited. We don't have time for spray and pray retrofit. Only with a universal, scalable way of understanding how heat loss is happening can we enable the efficient allocation of scarce retrofit resources.’


Charlie Delingpole, founder of Comply Advantage and Kestrix investor, said: ‘Without data, decisions aren't made, projects aren't financed, and outcomes aren’t verified. With a clear vision to become the data layer for building retrofit the industry so desperately needs in a way that is scalable and defensible, supporting Kestrix felt obvious.’

In the UK alone, 17% of emissions come from heating and cooling buildings, and 1.8 retrofits must happen every minute between now and 2050 for net zero targets to be met.


But industry lacks a scalable, accurate way of measuring heat loss across whole portfolios, leading to lost time, wasted resources, and overstretched budgets.


Kestrix helps housing providers, local governments, and energy utilities, map how heat is lost across portfolios or cities of buildings at once.


The new investment will accelerate the development of automated Rapid Thermal Performance Assessment (RaThPA) machine learning algorithms, which, in combination with aerial thermal imaging, will estimate energy performance of a building in seconds at a fraction of the cost of a traditional energy survey, requiring no in person site visit.




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