We've spent our careers building data businesses and running enterprise transformation programmes. Kallidin exists because we kept solving the same problem for different clients — and decided it was time to solve it at scale.
The idea took shape over Christmas 2025. By March 2026, Kallidin existed.
John Brodie and Warwick Beresford-Jones had spent fifteen years building and selling data analytics businesses together — Aquila Insight to Dentsu Merkle, Forth Point to Blend360. Two exits, the same co-founders, the same conviction that the next thing had to be bigger than what came before. Having seen clients struggle with multiple data challenges over the years, they knew that AI adoption for enterprises would have its challenges — and that most of them would be data related.
Sam Riddington had been watching the same pattern play out across three decades of enterprise transformation at IBM iX, Accenture, and Deloitte. The words changed. The industry didn't matter. Our AI programmes are stalling. We can't demonstrate ROI. We don't know what our organisation needs to look like in an AI world. He'd seen it too many times to believe it was a technology problem. It was a foundations problem.
Then John and Warwick noticed something about the platform they were beginning to design. The Autonomous Data Office — an AI agent workforce augmenting the traditional data team — would, if it worked as intended, have transformed how businesses like Aquila Insight and Forth Point had operated. It would handle what had traditionally consumed their analysts' time, running in parallel, around the clock, eliminating the bottlenecks. Their analysts could have focused on higher-value strategy and insight rather than routine execution.
Then came a second realisation. As AI agents proliferate inside enterprise systems — in sales, finance, operations, customer service — they all hit the same wall: they need verified data to act on, and they need it fast. Every agent deployment creates a new data dependency. The same system that serves a data science department could serve an entire agent ecosystem. The ADO wasn't just a better data team. It was the data infrastructure layer for the agentic enterprise.
That realisation sharpened everything. Kallidin is what happened next.
Two exits in data. Three decades of enterprise transformation. The experience behind Kallidin isn't theoretical.
John has built, scaled, and exited data businesses twice — Aquila Insight to Dentsu Merkle in 2017, Forth Point to Blend360 in 2023. He understands from the inside exactly what the ADO transforms, and why that transformation is now inevitable.
His work on The Data Lab's Governance Board and as Entrepreneur-in-Residence at the University of Edinburgh's AI Accelerator gave him a sustained view of where Scotland's data and AI ecosystem was heading — and where the structural gaps remained. Kallidin is the answer to those gaps.
LinkedInWarwick co-founded both Aquila Insight and Forth Point, and was the product and technical backbone behind two successful exits. He's been thinking about how to automate what data teams do for longer than most people have been aware the problem exists.
At Kallidin, his focus is the ADO itself — the agent architecture, the Unified Semantic Layer, and the orchestration logic that holds it together. The system's adversarial governance design reflects his conviction that AI outputs in enterprise contexts need to be proven right, not assumed correct.
LinkedInSam's career has been built around large-scale enterprise transformation — IBM iX, Accenture, Deloitte. Programmes at Vodafone, AstraZeneca, FIFA. Two decades of watching AI and data initiatives stall at the same point, for the same structural reasons.
At Kallidin, he leads the consulting practice and the AI Enablement methodology — the structured work that builds the foundations the ADO runs on. He is the person in the room who has seen the most failed AI programmes, and has the clearest view of exactly why they failed.
LinkedInWe've been around long enough to know what bad looks like. These aren't aspirations — they're the way we've run every business we've built.
The best outcomes come from genuine collaboration — sharing what we see, being honest when something isn't working, and staying invested in the result long after the engagement ends. We're partners in the problem, not vendors with a scope of work.
If an approach won't hold up, we redesign it. If an answer is wrong, we say so. The adversarial governance built into the ADO reflects the same standard we hold ourselves to in every engagement.
The consultants who scope your engagement are the ones who deliver it. No handoff to a junior team after the contract is signed. The people you work with have done this before — and are accountable to results, not just deliverables.
We'd rather tell you something uncomfortable and useful than something comfortable and wrong. That's true in the boardroom and it's true in the architecture — the Mathematical Critic in the ADO exists for exactly this reason.
Tell us where you're stuck. We'll tell you if we're the right fit.
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