AI & Machine Learning
Description
AI where it earns itsplace
A great deal ofAI work solves problems nobody had. We are more interested in the narrower setof cases where it genuinely changes the experience: content that adapts to theperson reading it, interfaces that respond to how someone is actually behaving,and repetitive work that no longer needs a human doing it.
Our startingquestion is whether AI is the right tool at all. Often a simpler rule, a betterinformation structure, or a clearer interface will outperform a model, costless, and be easier to maintain. When AI is the right answer, we say so andexplain why.
Personalisation and Adaptive Content
Experiences that change based on who is using them. Content that reorders itself by interest, recommendations grounded in real behaviour rather than guesswork, and journeys that adjust as a person moves through them.
Intelligent Interfaces and Assistants
Conversational interfaces, search that understands intent rather than matching keywords, and assistants that help users find what they need inside a large amount of information. Most useful where a catalogue or archive is too big to browse.
Automation and Workflow
Removing repetitive work from people who should be doing something else. Document processing, classification, routing, summarisation, and the routine steps that quietly consume a team’s week without appearing on anyone’s plan.
AI in Immersive and Interactive Experiences
Where our AI work meets the rest of the studio. Installations that respond to audience movement or crowd density, visuals generated live rather than pre rendered, and interactive environments that behave differently each time someone encounters them.
Start small, prove it,then scale
AI projectsfail most often by starting too large. We prefer to define one narrow,measurable use case, build it, and test whether it actually improves anythingbefore expanding. That keeps the initial cost contained and gives you evidencerather than a promise.
We are alsodirect about limitations. Models are wrong sometimes, they need data that isoften messier than expected, and they require maintenance after launch. Thoserealities are better discussed at the start of a project than discovered midwaythrough one.
Answers to the Questions Asked
The questionswe are asked most often about applied AI. If yours is not here, get in touchand we will answer it directly.
The reliable uses are narrower than the coverage suggests. Personalising content, understanding intent in search, processing documents, summarising information, and automating repetitive steps all work well today. Anything requiring judgement or accountability still needs a person.
Not always. Personalisation and automation usually need your own data, but many language and vision tasks now work with little or none. We assess what you have before recommending anything, since data quality is the most common reason AI projects stall.
It depends on scope, data readiness, and whether the system integrates with existing platforms. We usually recommend starting with one narrow use case, which keeps initial cost low and produces evidence before you commit to anything larger.
Data handling is agreed before work starts, including where information is stored, what leaves your systems, and which models are used. Where sensitive data is involved, we can design around processing that keeps it inside your own environment.
Not in the work we do. The useful applications remove repetitive tasks so people spend time on judgement, relationships, and creative decisions instead. Projects framed as headcount reduction usually disappoint on both counts.
Yes. We build AI into installations and interactive environments, where it can drive visuals that respond to movement, adapt content to crowd behaviour, or generate imagery live so no two visitors see quite the same thing.
Wondering whether AI fits?
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