01
A pilot that stalled.
The demo was brilliant and production never came. The problem is rarely the technology.
Built to be trusted,
by the team that runs it, the board that funded it, and the regulator who will ask.
Kallidin built KAL, an AI product for businesses that need to understand how it works, not trust a black box. That’s the difference between an answer and a verified answer you can stand behind and reproduce.
We deliver confidence, and the system that sustains it.
01
First, we deliver confidence: the evidenced case for what to do, and what it is worth. Then the foundations, so the system has something to stand on. Then we deploy KAL itself, and you are left with a working data office. Not a deck, not a platform you staff, not a team you hire.
02
Secondly, we deliver speed. KAL answers in minutes. The question that takes six weeks: brief written, ticket raised, analyst assigned, deck returned, question already moved on, comes back while you are still in the meeting, with the proof attached. Not a faster queue. No queue.
You’re probably here because one of these is true.
02
A backlog you can’t clear.
You can’t hire your way out of an ad hoc queue. Access is the bottleneck, not headcount.
03
Data foundations that won’t take the weight.
Everyone in the building knows it. Nobody wants to be the one who says the AI plan needs a data plan first.
04
Pressure to show AI returns.
The board has funded the experiments. Now it wants proof.
05
New in the role.
Your first ninety days set the tone. An early, visible win matters.
Drag, tap a card, or use the arrow keys.
It depends less on your industry than on the size of the gap.
You have a data team and a backlog they cannot clear.
Your analysts are good. That was never the problem. The problem is that the queue never empties, so your best people spend the week answering the same twelve questions instead of the ones that would move the business. KAL takes the queue. They take the judgement, and the function gets more valuable to the business rather than smaller.
You have two analysts and a business that needs twenty.
At that ratio you are not running a backlog, you are running a ceiling. Questions stop being asked because everyone already knows there is no capacity to answer them. KAL gives a small team the output of a large one: the engineering, the modelling, the governance and the write-up, running behind the people you already have.
You have no data function to speak of.
Most mid-sized businesses are sitting on data they have never used, because the cost of the people who could use it was never justifiable against the return. That arithmetic has changed. You no longer have to build a data office in order to have one.
You are not in a regulated industry at all.
Good. We built KAL for businesses that have to defend every number to a regulator, which is the hardest version of this problem. You get the same rigour with an easier audience.
You don’t have to take our word for it.
Not for the advice, not for the build, not for the model. Everything we do is built to show its working, including how we handle your data: every answer is checked by an independent AI agent, and arrives with a confidence score and a proof you can re-run, your data stays in your own environment in the UK, and your prompts run on infrastructure the model providers never touch. The full answers to the questions your security team will ask are published openly.
UK data residency
The audit trail and checking layer
Role-based access and control
Versioning and change control
A clean, documented exit
KAL, the Autonomous Data OfficeThe daily work of a data team you can question. Ask in plain language, get an answer, and see exactly how it got there, checked and traced.
ConsultingWe find and fix what is broken in the foundation: access, governance, and the quality of the data itself. The unglamorous work that decides whether AI ships.
Technical buildThe middle ground. We build the specific capability you need now, ready to grow into the full office later.
What does Kallidin actually do?
Kallidin runs the data function of an enterprise. We call it KAL, the Autonomous Data Office: AI agents that answer business questions from your data, with every answer checked and fully auditable. Alongside it sits consulting that fixes the foundations those answers depend on. It is built to the bar a regulator would set, because that is the hardest version of this problem.
Most enterprise AI programmes stall for the same reason. The foundations underneath, the access, the governance, the quality of the data itself, were never built for what AI is now being asked to do. So the pilot demos well and production never comes. Kallidin works on both sides of that problem. The consulting builds the foundation. KAL runs the office on top of it: ask it something in plain language, get an answer, and see exactly how it got there.
How the work runs.
01The diagnostic.
A short, flat fee consulting engagement that maps your data estate, finds why things have stalled, and designs the fix. The lowest risk way to start, with no obligation to go further.
02The foundation.
We build or repair what the answers depend on: access, governance, the plumbing nobody shows the board. The unglamorous work that decides whether AI ships.
03Answers you can audit.
KAL is deployed on these foundations. You ask questions in plain language, and every answer arrives checked, scored and traced, with errors caught before they reach you.
Not sure which step you need? Book a discovery call and we’ll tell you straight.
Who are Kallidin?
The people are the proof.
Kallidin is new. The people behind it are not. They’ve spent their careers on exactly this problem.
John Brodie and Warwick Beresford-Jones built two data businesses together and sold them both, Aquila Insight to Merkle in 2017 and Forth Point to Blend360 in 2023.
Sam Riddington has spent more than 25 years in enterprise consulting with firms including IBM, Accenture and Optima. Anders Uhrenholt, our Chief Engineer, holds a PhD in machine learning and built recommendation systems at Amazon that served more than 500 million people. The patterns we work from come from those careers, the stalled programmes and the backlogs that would not clear. We’ve seen how this goes wrong, and what it takes to make it work.
John BrodieCo-founder. Built and sold Aquila Insight, to Merkle in 2017.
Warwick Beresford-JonesCo-founder. Built and sold Forth Point, to Blend360 in 2023.
Sam Riddington25+ years in enterprise consulting. IBM, Accenture and Optima.
Anders UhrenholtChief Engineer. PhD in machine learning; Amazon recommenders serving 500m+ people.
Why choose Kallidin over a bigger firm?
The knowledge stays.
A larger firm has the domain knowledge. So do we. The difference is what happens to it. They sell it by the day, in people you rent, and when the engagement ends that knowledge walks out with them. We spent our careers running data functions inside banks, telcos and utilities, and then we built that judgement into KAL, so it stays with you. We operate in days where a larger firm operates in months, with no bench to feed and no partner to route through. At the end of it you have a working data office, not a headcount plan.
Start with a conversation.
The first step is a discovery call, and the usual next step is the diagnostic: two to four weeks, flat fee, that finds what is broken and designs the fix. Bring the questions your business keeps failing to answer.