Professional AI consulting practice
Rubytech puts AI to work where the operation is hard: many systems that do not talk to each other, work that spans jurisdictions and entities, and high-stakes decisions where a wrong answer is expensive. We find the return, build on data you can trust, and keep people in control at every step. And we do not stop at advice. We build and run AI across whole verticals.
What we do
Most organisations do not struggle with AI because the models are weak. They struggle because the operation underneath is tangled: information sits in a dozen systems that were never meant to talk, the same customer is recorded three different ways, and a single piece of work crosses several entities and more than one set of rules. We help you cut through that, put AI to work where the return is plain, and keep a person in charge of the outcome.
We start by finding the project with the quickest, clearest return, then prove it before anyone commits to a wider programme.
We give your information one trusted home, so AI works from facts your team agrees on rather than from scattered copies that disagree.
Every workflow we build runs under human review. The operator decides what happens; the system does the heavy lifting.
AI consulting services
A clear set of services, each tied to an outcome you can measure and to the return on your AI deployment. We take on as much or as little of the work as you need, from a first opinion to running the system day to day.
We map where AI can genuinely help, rank the options by return and effort, and give you a short, honest plan you can act on.
Outcome: a costed shortlistWe automate the repetitive, rules-heavy work, the reconciliations, the chasing, the re-keying, so your people spend their time on judgement, not admin.
Outcome: hours returnedWe build a single, trusted source for your information, so every system and every model reads from the same agreed set of facts.
Outcome: one source of truthWe connect the tools you already run so they work as one. No more manual hand-offs between systems that were never meant to talk.
Outcome: fewer manual hand-offsWe put clear ownership, oversight, and records around every AI system, so you always know who owns a decision and can explain how it was made.
Outcome: decisions you can stand behindWe do not stop at the slide deck. We build, deploy, and, where you want it, run the system so the value actually lands.
Outcome: systems in productionA productised first step
A fixed-scope engagement that cuts through your operational complexity and tells you plainly where AI is worth the effort and where it is not. No open-ended discovery, no standing retainer. One clear piece of work with a clear end point.
We map how the work actually gets done, find where the time and money leak away across your systems and entities, and score the opportunities by return and effort. You end up with a written assessment report and a costed shortlist of AI opportunities you can act on, whether you act on it with us or not.
Our approach
We earn the right to do more by getting the first thing right. The method is the same every time: cut through the complexity, put AI where the return is plain, and keep a person in control at every step.
We learn how the work really gets done and where the time and money leak away.
We choose the project with the highest return for the least effort and risk.
We give your data one trusted home so everything after it stands on firm ground.
We put orchestration and agents to work, with a person reviewing and approving.
Once one project pays for itself, we extend the same foundation to the next.
People stay in control. The system handles the volume and the repetition; your operators set the rules, review the work, and hold the final say. That is how AI belongs in an operation where a wrong answer is expensive.
Selected work
We do not just advise on AI. We build it and we run it. These are four products of our own, live today and built principally on Anthropic's Claude models. Two are the AI environment and the training behind it; two are complete deployments running the day-to-day operations of an entire industry. They are the evidence that we can take AI into a complex operation and make it work.
The AI environment we install on a client's own server: Claude Code joined to a persistent knowledge graph that holds every person, document, conversation, and decision they have touched, with email, messaging, research, and document production wired in. It is the working version of the architecture we recommend, built by us and run by us.
Claude Code · knowledge graph · agent orchestration getmaxy.com → EducationA hands-on course, in London and online, that teaches knowledge workers to set Claude up around their own work without writing code. Twenty-eight modules across five stages, from the basics of the models through bringing your archives into a structured data layer to running the whole setup day to day. It is our applied Claude knowledge, written down and taught.
Claude fundamentals · data layer · applied practice maxy.institute → Deployment · ConstructionA configured deployment of our environment running a construction vertical's operational processes end to end. Building work is a hard operation to run: many trades, many sites, documents and approvals moving between parties who each keep their own version of the truth. SiteDesk puts AI to work across that, under human control.
Vertical deployment · construction operations sitedesk.online → Deployment · Estate AgencyA configured deployment built for UK estate agency, running the operational work of the whole vertical: enquiries, listings, correspondence, and the paperwork that moves a sale along. It is the same architecture as Maxy, shaped to how an estate agency actually works, and run day to day.
Vertical deployment · UK estate agency realagent.network →Insights
Short, plain pieces on the questions leaders of complex organisations put to us most: why more integrations rarely fix a fragmented business, who owns an AI decision when it goes wrong, and what the new rules ask of an operation that already spans borders.
Each new connection between two systems is one more thing to maintain, and one more place for the numbers to disagree.
When every tool holds its own copy of the truth, your team spends its days reconciling. Wiring the tools together more tightly just moves the mess around.
The durable fix is a shared data foundation: one trusted source every system reads from. AI then works from facts your people already agree on, and the reconciliation simply stops.
Read the view →Most teams are renting the layer they run on. The choice is whether to keep renting or to own the data and run the model directly on it.
AI agents arrive in waves, from a tool one person uses to AI woven through how the business runs. Knowing which wave you are in, and which one you are moving to, frames every other decision.
The durable position is to keep your data in a structure you own, a knowledge graph, and use the model directly on it. You gain the model's growing capacity with every release, and you never give away control.
Read the view →Responsible AI is less about principles on a wall and more about who answers when something goes wrong.
The questions that matter are simple. Who owns this system? What data trained it? Can we explain a decision to the person it affected? Can we show our working?
Done well, this is not a brake on progress. Clear ownership and good records are what let you scale AI with confidence instead of crossing your fingers.
Read the view →If your work already spans more than one country, the cleanest move is to hold every market to the strictest rule that applies to you anywhere.
Europe's AI law sorts systems by how much harm they could do, and the heavier the risk, the more you have to prove. Compliance is one strand of this; the harder part is running one operation across several sets of rules at once.
Start now with a plain inventory: which AI systems you run, what each one decides, and who owns the outcome. Most of the effort is records and oversight, not new technology, and those take time to put in place.
Read the view →About Rubytech
Rubytech is a professional AI consulting practice. We work with organisations where the operation is genuinely hard: many systems that do not talk to each other, work that spans jurisdictions and entities, and high-stakes decisions where a wrong answer is expensive.
We have earned that judgement in some of the most demanding settings there are. Our experience runs across banking, insurance, healthcare, data, and science, sectors where the stakes are high and the rules are strict. We treat that as evidence, not as the limit of who we serve: if we can handle AI in those settings, we can handle it in yours. What carries over is the ability to take a tangled, multi-system operation and make AI work inside it safely.
That depth shows up as judgement: knowing which problem is worth solving first, which data you can trust, and where AI earns its place rather than adding risk. We work alongside business, division and product leaders to maximise the return on your AI deployment, as senior independent consultants, so you deal with the people doing the thinking, not a chain of hand-offs.
The proof is in what we have built. Our own products run on Anthropic's Claude models in production, day to day, including complete deployments that run the operations of an entire industry. The work above is the evidence under the capability, not a portfolio of slides.
We provide services in the United Kingdom and across the EEA.
Start a conversation
A first conversation costs you nothing but an hour. Tell us a little about your organisation and what you are trying to solve, and we will tell you honestly whether AI is the right answer for the return you are after, and if it is, where to begin.