Services
AI Products
What does designing an AI product actually involve? Deciding where the model helps, showing people how sure it is, and giving them a fast way to fix it when it’s wrong.
Adding AI to a product is rarely a modelling problem. The model usually works. What fails is everything around it: people can’t tell whether to trust an answer, there’s no obvious way to correct one, and nobody can explain afterwards why the system said what it said.
That’s design work, and it has to happen before the feature ships — because trust, once lost on an AI feature, doesn’t come back on the second release.
The hard part isn’t the model. It’s everything around it.
A model that’s right 90% of the time is excellent. It also means one answer in ten is wrong, and your interface decides whether that’s a minor correction or a reason to stop using the product.
We design for the wrong answer first. What it looks like, how someone spots it, how quickly they can override it, and what the system does with that correction. Get that right and a 90% model feels reliable. Get it wrong and a 99% model feels like a liability.
- 22 years designing digital products
- 20+ AI features shipped
- 100% of engagements include usability testing
What you get
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Use-case definition
Deciding where the model genuinely helps and where it adds risk for no gain. Most AI features that fail were never worth building — the work was deciding that before the sprint, not after.
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Confidence and uncertainty
How the interface shows how sure the system is, in terms a user can act on. “Probably” is not a percentage, and a percentage is not an explanation.
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Correction paths
How someone overrides a wrong answer in seconds rather than abandoning the task. This is the single most neglected part of AI product design and the one that decides whether people keep using it.
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Failure and empty states
What happens when the model returns nothing, times out, or produces something unusable — designed deliberately, not left to whatever the API happens to return.
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Prompt and output design
The shape of what goes in and what comes back. Structured output that the interface can render properly beats a wall of prose a user has to parse.
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Evaluation
How you’ll know whether it’s working after launch, defined before launch. Usually a mix of task success, correction rate and abandonment rather than model accuracy alone.
How it works
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01
Where AI earns its place
We map the workflow and mark where a model genuinely helps, where a rule would do the job more reliably, and where it shouldn’t be used at all. Some of our most useful recommendations are “don’t put AI here”
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02
Trust design
Confidence, provenance and correction, designed before any screens. These decide whether people use the feature, and they’re very hard to retrofit.
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03
Interface design
Screens and states, including every way the model can disappoint: wrong, empty, slow, partial.
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04
Prototype and test
With real users and real model output, including deliberately bad output. Testing only the happy path tells you nothing about an AI feature.
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05
Evaluation and handoff
Specs for your engineers, plus the measures you’ll watch after launch.
Questions
Do you build the AI, or just design it?
We design it, and work alongside whoever is building it. We’re comfortable in the technical conversation — context windows, latency, structured output, evaluation — without pretending to be your ML team.
We don’t know if AI is right for our product. Can you help us decide?
That’s often the most valuable part of the engagement. A short discovery can tell you where a model helps, where a rule is better, and where the whole idea should be dropped — usually for a fraction of what building the wrong thing costs.
How do you design for something that gives different answers every time?
By designing the frame rather than the content: how uncertainty is shown, how output is structured, what happens when it’s wrong, and what the user can do about it. The variability is a given; the handling of it is the design.
What about privacy and the data we send to a model?
It’s a design question as much as a legal one — people need to know what’s being sent, and for some products they need to be able to decline. We’ll design that disclosure with you, though your own legal counsel should review the position itself.
How long does an AI feature take to design?
A single feature, four to six weeks. A product built around AI, three to five months. We’ll tell you which yours is after the first conversation.
What does it cost?
It depends on scope. We work to a minimum engagement so that research and testing don’t get cut to fit a budget — the two things that matter most on AI features. We’ll give you a range on the first call. +91 85660 15214
Related work
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