tem Raises $75M to Modernise Energy Trading

In a major climate-and-fintech crossover moment, London-based tem has secured fresh growth capital to modernise how businesses buy, sell, and manage energy through automated, AI-assisted transactions. The round underlines a wider shift the ai world organisation tracks closely: critical infrastructure industries are being rebuilt with software-first rails, and those rails are becoming prime territory for ai conferences by ai world and the ai world summit conversations in ai world summit 2025 / 2026 programming.

Why energy deals still feel “stuck in the past”

Energy procurement and trading can still be surprisingly opaque for many businesses, with contracts that are hard to compare, fees that are not always easy to interpret, and workflows that rely on legacy processes rather than modern transaction rails. That friction matters because the stakes are rising fast: the same forces reshaping the digital economy—data centres, AI workloads, and electrification—are expected to drive a 165% increase in demand by 2030, putting more pressure on pricing, risk management, and operational efficiency.

tem’s premise is that the pain isn’t just about “getting a better tariff,” it’s about rebuilding the transaction layer that sits underneath the market so outcomes become clearer, faster, and cheaper for everyone involved. The company describes its direction as becoming the “Stripe of energy,” signalling a push toward standardised, automated, API-like infrastructure that can make energy transactions feel more like modern payments. From the ai world organisation perspective, this is exactly the kind of “infrastructure story” that tends to ripple across ecosystems, because once a transaction layer becomes easier to use, it attracts new product builders, new distribution, and new competition.

This is also why energy is showing up more often in AI leadership discussions: as energy demand grows, businesses increasingly need better forecasting, more transparent contract logic, and real-time operational control, not just periodic broker-led renegotiations. At the ai world summit, these themes typically land at the intersection of AI adoption and business strategy—how automation changes unit economics, how data unlocks better pricing, and how platforms reshape markets when they scale. For ai world organisation events and ai conferences by ai world, tem’s update is a clean example of a broader pattern: “AI” isn’t only a feature, it’s becoming part of the operating system for transaction-heavy industries.

tem’s approach: AI-driven transaction infrastructure plus a neo-utility layer

tem says it is building AI-based infrastructure for energy transactions, aiming to automate contracts, make pricing clearer, and reduce costs for customers. The company positions its platform as a way to deliver “clear transactions” and claims it can reduce costs by up to 30%, alongside offering what it calls a modern neo-utility interface. That combination—back-end transaction automation plus a front-end that behaves like a product, not paperwork—matters because energy decisions often fail not due to lack of intent, but because complexity makes consistent action hard.

In tem’s product language, Rosso is the AI engine designed to remove fees and automate contracts, while RED is the user-facing neo-utility experience for buying, selling, and managing energy with better pricing. The company highlights “full transparency with no hidden costs,” potential savings “through data-driven pricing,” and the advantage of growing transaction volumes, noting it manages 2 TWh each year. tem also frames RED as the layer that gives businesses and brokers access to its transaction infrastructure, which it believes can unlock transparency and price innovation for energy users.

From a market-structure lens, tem is arguing that the real product is not just switching, consulting, or brokerage, but a repeatable transaction system that can be adopted at scale by multiple market participants. That thesis maps closely to what many builders discuss at the ai world summit and ai world summit 2025 / 2026 tracks: once a platform turns messy, bespoke workflows into a standardised flow, you can build new services on top—analytics, risk tooling, new financing models, and partner-led distribution. For the ai world organisation, stories like this are useful because they translate “AI in energy” from a vague concept into a practical operating model: automate the contract logic, standardise transactions, and use data to price more efficiently.

The $75M Series B: who backed tem and what they’re funding

tem has raised $75 million in an oversubscribed Series B round led by Lightspeed Venture Partners, with Paul Murphy joining the company’s board. The round also included participation from Hitachi Ventures, Voyager Ventures, Schroders Capital, AlbionVC, Atomico, and Allianz, bringing tem’s total funding to $94 million. Oversubscription is worth noting not as a hype marker, but as a signal that multiple investors appear to agree the company is tackling a large, structurally inefficient market where software leverage can compound.

The investors are effectively underwriting two bets at once: first, that demand-side pressure (including AI-driven demand growth) makes transaction efficiency more valuable; and second, that a well-designed transaction layer can become a durable “system of record” for energy procurement and trading-like workflows. tem’s pitch is that it is doing for energy what fintech did for banking by creating a market that can operate transparently, efficiently, and at scale, which is a classic infrastructure narrative in venture investing. If that narrative holds, the upside is not limited to a single product experience, because a transaction layer can expand through integrations, partnerships, and embedded distribution.

For the ai world organisation, this is the kind of financing event that helps explain why AI is increasingly tied to infrastructure outcomes: investors aren’t only funding “models,” they’re funding platforms that reorganise how value moves through industries. That’s a practical reason these topics belong in ai world organisation events and ai conferences by ai world—because they shape how enterprises budget, how risk is managed, and how the next generation of B2B products gets built. It also fits with the positioning of The AI World Summit as a place where AI pioneers, educators, policymakers, and industry leaders connect around real-world adoption, not just demos.

Traction signals: volume, customers, and proof the workflow matters

tem reports that in 2025 it handled $300 million in annual gross transaction value and 2 TWh for more than 2,600 UK customers. It also names examples of customers that include Boohoo, Fever-Tree, Silverstone, and Newcastle United FC, pointing to a cross-sector user base rather than a single niche. These metrics matter because energy platforms can look compelling in theory, but scaling transaction volume is the harder test—especially in markets where legacy processes, trust, and compliance norms can slow adoption.

Just as importantly, tem is trying to serve multiple participant types (businesses and brokers) rather than forcing a single-channel model, which can accelerate distribution if incentives align. In many industries, the fastest way to scale a transaction layer is to make it valuable for intermediaries while still improving transparency and outcomes for end users, and tem’s product framing suggests it is aiming for that dual adoption. When this model works, it can reduce not only direct costs but also the “coordination tax” that comes from manually managing renewals, renegotiations, and contract comparisons across multiple sites or business units.

For ai world summit 2025 / 2026 audiences, tem’s traction is a useful case study because it offers a measurable bridge between “AI automates a process” and “AI changes a market,” with transaction volume and energy volume acting as concrete indicators. It also reinforces a broader enterprise lesson that comes up frequently at the ai world summit: software wins when it collapses complexity into repeatable workflows that non-specialists can operate confidently. That’s one reason the ai world organisation often emphasises practical, tactical learning formats across its global programming, including workshops and real-world case studies.

What comes next: expansion to Texas and Australia, and a bigger data flywheel

tem says the new $75 million will be used to grow in the UK and to launch in Texas and Australia, markets it views as needing more efficient transaction infrastructure. The company also plans to make its Rosso engine available to more companies and expand its data pool to improve pricing. Put simply, the plan is to turn execution into a flywheel: more transactions create more data, more data improves pricing and automation, and improved outcomes attract more participants.

The competitive landscape tem highlights includes Enel X, described as operating in global energy management, and Octopus Energy, described as focused on the UK retail market. That comparison is telling because it implies tem doesn’t want to be boxed into one category; instead, it wants to sit closer to the market’s transaction plumbing while still delivering a strong end-user experience through RED. If tem succeeds, it could become infrastructure that existing energy companies, neo-utilities, and new brands adopt to unlock fairer and more efficient transactions at scale.

This “infrastructure that others can adopt” framing is particularly relevant for the ai world organisation’s community, because it’s where partnerships, ecosystems, and platform strategy become as important as raw technology. It’s also a natural fit for ai world organisation events: cross-industry operators—marketers, growth leaders, founders, agencies, and enterprise teams—can learn how these platforms scale, how they position trust, and how AI is operationalised beyond experimentation. If you’re curating topics for the ai world summit calendar, tem’s trajectory sits neatly in the overlap of AI, climate tech, fintech-like infrastructure, and the future of enterprise operations—exactly the kind of intersection where meaningful collaboration tends to happen.

As The AI World Organisation describes it, its summits are designed to inspire, educate, and connect the global AI community, bringing together leading minds in AI and business for actionable insights. The AI World Summit Singapore 2026, for example, is positioned as a practical and tactical event with formats that include keynotes, panels, interactive workshops, live case studies, networking, and brand-building sessions, and it is described as application-only. For anyone tracking where AI is going next—especially into “boring but essential” transaction layers—bringing case studies like tem into the ai world summit 2025 / 2026 conversation can help leaders move from hype to implementation.