Product design for AI startups and AI-native software
AI products fail less often on model quality than on product design: people don't know what to ask, can't tell when to trust the answer, and can't follow what an agent did. We design the product around the model so it gets used, and used safely.
Who we work with
AI startups
Turning a capable model into a product people adopt: positioning the capability, onboarding, and the core workflow.
AI SaaS
Adding AI to an existing SaaS product without breaking the workflows customers already rely on.
Agentic products
Software that acts on the user's behalf, where delegation, approval and oversight are the product.
What we design
AI interfaces
Prompting, results, sources and editing designed around how much error the task can tolerate.
AI workflows
Where AI sits in a real job: what it drafts, what a person checks and what happens next.
AI product strategy
Which problems benefit from AI, which don't, and how to test that before building.
Agent oversight
Status, approvals, audit trails and review queues for work an agent did on someone's behalf.
Our point of view
Design the wrong answer first. If an AI feature cannot gracefully handle a confident mistake, it is not ready, however good the model is.
Match autonomy to reversibility. Suggest when mistakes are cheap, draft when people will check the work, and require approval before anything that can't be undone.
Show the basis, not a disclaimer. Sources and the data an answer used build calibrated trust; generic warnings teach people to ignore warnings.
What product leaders say
“Working with DesignIt was a thoughtful and collaborative experience. Their approach was deeply rooted in understanding user pain points and bringing data-backed insights into the product. The team paid close attention to edge cases and real-world usage, which helped create solutions that truly resonated with users.”
Public LinkedIn recommendation, reproduced verbatim.
Frequently asked questions
What do AI startups most often get wrong in product design?
Shipping a blank prompt box instead of guiding a task, designing only the happy path, and giving no way to see or correct what the AI did. Those choices make strong models feel unreliable.
How should an AI SaaS product onboard new users?
With example tasks rather than an empty prompt, honest limits up front, and a first success in the first session that shows what the product is for.
Can you show us AI work you've done?
Much of it is under NDA, so we walk through relevant work on a call rather than publishing it.
Tell us what your AI does, and what happens when it's wrong
Much of our AI work is under NDA, so we walk through relevant examples on a call.