TransHumanity’s vision is to empower faster, smarter human decisions by transforming data into accessible intelligence using large language model based agentic AI. 

Agentic AI refers to artificial intelligence systems that collaborate with people to reach specific goals, understanding and responding in plain English. These systems use AI “agents” — models that can gather information, make suggestions, and carry out tasks in real time — helping people solve problems more quickly and effectively.

TransHumanity’s first product, AptIq, is designed to help transport authorities quickly analyse transport data and models, turning days of analysis into seconds. 

By simply asking questions in plain English, users can gain instant insights to support key initiatives like congestion reduction, road safety, creation of business cases and net-zero targets.

Dr Haitao He, Co-founder and Director of TransHumanity, said: “I am proud to see my rigorous research translated into trusted real-world AI innovation for the transport sector. With this investment, we can now realise my Future Leaders Fellowship vision, scaling a technology that empowers authorities across the UK to deliver integrated, net-zero transport.”

Developed from rigorous research by Dr Haitao He, a UKRI Future Leaders Fellow in Transport AI at Loughborough University, AptIq, previously known as TraffEase, has already garnered significant recognition. 

The technology was named a Top 10 finalist for the 2024 Manchester Prize for AI innovation and was recently highlighted as one of the Top 40 UK tech start-ups at London Tech Week by the UK Department for Business and Trade.

Adam Beveridge, Investment Principal at SFC Capital, said: “We are excited to back TransHumanity. The combination of cutting-edge research, a proven founding team, clear market demand, and positive societal impact makes this exactly the kind of high-growth venture we are committed to supporting.”

AptIq is currently in a test deployment with Nottingham City Council and Transport for Greater Manchester, with plans to expand to other city, regional, and national authorities across the UK within the next 12 months.

With a product roadmap that includes diverse data sources, advanced analytics and giving the user full control over the AI tool when required, interest from the transport sector is already high. Professor Nick Jennings, Vice-Chancellor and President of Loughborough University, noted: “I am delighted to see TransHumanity fast-tracked from lab to investment-ready spinout.

This journey was accelerated by TransHumanity’s selection as a finalist in the prestigious Manchester Prize and shows what’s possible when the University’s ambition aligns with national innovation policy.”

An oversimplified approach I have taken in the past to explain wisdom is to share that “We don’t know what we don’t know until we know it.” This absolutely applies to the fast-moving AI space, where unknowingly introducing legal and compliance risk through an organization’s use of AI is a top concern among IT leaders. 

We’re now building systems that learn and evolve on their own, and that raises new questions along with new kinds of risk affecting contracts, compliance, and brand trust.

At Broadcom, we’ve adopted what I’d call a thoughtful ‘move smart and then fast’’ approach. Every AI use case requires sign-off from both our legal and information security teams. Some folks may complain, saying it slows them down. But if you’re moving fast with AI and putting sensitive data at risk, you’re also inviting trouble if you don’t also move smart.

Here are seven things I’ve learned about collaborating with legal teams on AI projects.

1. Partner with Legal Early On

Don’t wait until the AI service is built to bring legal in. There’s always the risk that choices you make about data, architecture, and system behavior can create regulatory headaches or break contracts later on.

Besides, legal doesn’t need every answer on day one. What they do need is visibility into the gray areas. What data are you using and producing? How does the model make decisions? Could those decisions shift over time? Walk them through what you’re building and flag the parts that still need figuring out.

2. Document Your Decisions as You Go

AI projects move fast with teams needing to make dozens of early decisions on everything from data sources to training logic. So, it’s only natural that a few months later, chances are no one remembers why those choices were made. Then someone from compliance shows up with questions about those choices, and you’ve got nothing to point to.

To avoid that situation, keep a simple log as you work. Then, should a subsequent audit or inquiry occur, you’ll have something solid to help answer any questions.

3. Build Systems You Can Explain

Legal teams need to understand your system so they can explain it to regulators, procurement officers, or internal risk reviewers. If they can’t, there’s the risk that your project could stall or even fail after it ships.

I’ve seen teams consume SaaS-based AI services  without realizing the provider could swap out a backend AI model without their knowledge. If that leads to changes in the system’s behavior behind the scenes, it could redirect your data in ways you didn’t intend. That’s one reason why you’ve got to know your AI supply chain, top to bottom. Ensure that services you build or consume have end-to-end auditability of the AI software supply chain. Legal can’t defend a system if they don’t understand how it works.

4. Watch Out for Shadow AI

Any engineer can subscribe to an AI service and accept the provider’s terms without knowing they don’t have the authority to do that on behalf of the company.

That exposes the organization to major risk. An engineer might accidentally agree to data-sharing terms that violate regulatory restrictions or expose sensitive customer data to a third party.

And it’s not just deliberate use anymore. Run a search in Google and you’re already getting AI output. It’s everywhere. The best way to avoid this is by building a culture where employees are aware of the legal boundaries. You can give teams a safe place to experiment, but at the same time, make sure you know what tools they’re using and what data they’re touching.

5. Help Legal Navigate Contract Language

AI systems get tangled in contract language; there are ownership rights, retraining rules, model drift, and more. Most engineers aren’t trained to spot those issues, but we’re the ones who understand how the systems behave.

That’s another reason why you’ve got to know your AI supply chain, top to bottom. In this case, when legal needs our help in reviewing vendor or customer agreements to put the contractual language into the appropriate technical context. What happens when the model changes? How are sensitive data sets safeguarded from being indexed or accessed via AI agents such as those that use Model Context Protocol (MCP)? We can translate the technical behavior into simple English—and that goes a long way toward helping the lawyers write better contracts.

6. Design with Auditability in Mind

AI is developing rapidly, with legal frameworks, regulatory requirements, and customer expectations evolving to keep pace. You need to be prepared for what might come next. 

Can you explain where your training data came from? Can you show how the model was tested for bias? Can you justify how it works? If someone from a regulatory body walked in tomorrow, would you be ready?

Design with auditability in mind. Especially when AI agents are chained together, you need to be able to prove that identity and access controls are enforced end-to-end. 

7. Handle Customer Data with Care

We don’t get to make decisions on behalf of our customers about how their data gets used. It’s their data. And when it’s private, it shouldn’t be fed to a model. Period. 

You’ve got to be disciplined about what data gets ingested. If your AI tool indexes everything by default, that can get messy fast. Are you touching private logs or passing anything to a hosted model without realizing it? Support teams might need access to diagnostic logs but that doesn’t mean third-party models should touch them. Tools are rapidly evolving that can generate comparable synthetic data devoid of any customer private data that could help with support use cases for example, but these tools and techniques should be fully vetted with your legal and CISO organizations prior to using them. 

The Reality

The engineering ethos is to move fast. But since safety and trust are on the line, you need to move smart, which means it’s okay if things take a little longer. The extra steps are worth it when they help protect your customers and your company.

Nobody has this all figured out. So ask questions by talking to people who’ve handled this kind of work before. The goal isn’t perfection—it’s to make smart, careful progress. For enterprises, the AI race isn’t a question of “Who’s best?” but rather “Who’s leveraging AI safely to drive the best business outcomes.” 

As Random Contrast Learning™ enters a new chapter of growth and adoption, Lumina AI announces the return of co-inventors, Ben and Sam Martin, to lead groundbreaking research and unlock new frontiers in machine learning.

TAMPA, Fla., Sept. 16, 2025 /PRNewswire/ — Lumina AI is proud to announce that Ben Martin and Sam Martin, co-inventors of RCL with Dr. Morten Middelfart, are rejoining the company as AI Research Scientists to support its next phase of growth.

The brothers bring distinct but complementary perspectives to RCL’s continued development. Ben, whose academic background is in philosophy and Husserl’s phenomenology, brings a foundational lens to RCL, supporting ongoing research into the algorithm’s theoretical structure and how the development of machine learning can draw upon models of human consciousness. Sam, whose technical background focuses on applied machine learning and algorithm performance, will focus on driving research on algorithmic scalability, and comparative performance against state-of-the-art machine learning methods.

“In machine learning, simplicity scales,” said Dr. Morten Middelfart, Chief Data Scientist of Lumina AI.“Ben and Sam understood that from day one, and their return marks a renewed focus on delivering clear, fast, and reliable models that work without unnecessary complexity.”

The Martins will contribute to Lumina’s expanding research footprint, including initiatives around hybrid model architectures, alternative learning systems, and long-term theoretical implications of machine intelligence. Their work will guide both internal development and external co-innovation partnerships.

“Welcoming Ben and Sam back to Lumina is both personally meaningful and strategically aligned with our mission,” said Allan Martin, CEO of Lumina AI. “As two of the three original minds behind RCL, their vision has shaped our algorithm from inception. Their return ensures that the future of RCL will proceed with both conceptual rigor and innovation. “

About Lumina AI

Lumina AI is redefining machine learning with Random Contrast Learning™ (RCL), a novel algorithm that achieves state-of-the-art accuracy while training rapidly on standard CPU hardware. By eliminating the need for GPUs, Lumina makes advanced AI more accessible, cost-effective, and sustainable.

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SOURCE Lumina AI