A generic AI system fails in a regulated enterprise for reasons that are specific rather than general: a named regulation, a data shape nobody documented, a failure mode the business already knows about. Each section below starts from those.
Introduction transcript. Client: Could the same AI work in every industry? Qubi: Same little me. Very different rules. Qubi: We start with your data, your regulations, and the work itself. Client: So we build around the tricky bits? Qubi: Exactly. That is where the good design starts. Qubi: Pick your industry. Let’s find the right fit.
Most of what an AI system needs is the same everywhere: retrieval that finds the right passage, evaluation that catches regression, an interface that fails visibly rather than silently. We do not think the underlying engineering is meaningfully different by sector, and a consultancy claiming otherwise is usually selling the same architecture six times with the nouns swapped.
What genuinely differs is the constraint set. Those constraints change the design of the system, not the label on the marketing page — and that is what these sections are about.
Each one opens onto the use cases we are asked about there, and the problems each use case has to solve.
AI in banking, lending and insurance where decisions must be explainable and auditable.
Explore Financial Services →AI over clinical and administrative data, built for privacy and traceability.
Explore Healthcare & Pharma →AI for contract review, research and discovery, with citation as a hard requirement.
Explore Legal & Compliance →AI for merchandising, support and demand forecasting in retail.
Explore E-Commerce & Retail →AI features inside a software product, and AI inside the team that runs it.
Explore SaaS & Technology →AI for production, quality and supply chain in industrial settings.
Explore Manufacturing →Claims triage, underwriting support and fraud detection, designed for a regulated decision that a person still signs.
Explore Insurance →AI for public bodies: procurement reality, data sovereignty, transparency duties and the records an audit will ask for.
Explore Public Sector →Document pipelines, exception handling and planning support for freight, ports and supply chains.
Explore Logistics & Freight →Every output attributable to a source and a model version. In a regulated sector this is the difference between a system that survives an audit and one that gets switched off after the first question about it.
Systems that escalate when uncertain rather than answering confidently. Retrieval does not eliminate model error; it makes it detectable and attributable, which is the true claim and the more useful one.
A regression gate built from real examples of your task, running after launch rather than only before it. Without it, quality decays invisibly and nobody can say when it started.
The constraint that sets the design, one line per sector.
Facts about how we work, not measured client outcomes.
No. These six are where we have written the constraints down, not the boundary of what we build. The first conversation in an unlisted sector is the same one: what decision does this system influence, who is accountable for it, and what does the regulator or the customer need to be able to see.
No, and we would be sceptical of anyone claiming to. What is sector-specific is the retrieval corpus, the evaluation set, the escalation policy and the audit trail. The model underneath is chosen per workload from the same families we would use anywhere.
More than the model choice and less than people fear. It mostly determines what has to be recorded, what has to be explainable, and where a human has to remain accountable — all of which are architectural decisions best made in week one rather than discovered during a security review.
Tell us the job you would hand over if you trusted a machine with it. In one 30-minute call we will tell you whether it should, what it would take — and if it should not, we will say so.
What we built, what stage it is at, and a link to the live thing wherever there is one.
yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.
Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.
Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
