Conversational AI Heuristic Review
A confidential heuristic review of a chat and AI assistant experience for a global consumer e-commerce brand, woven through a product used by millions. We evaluated it against usability principles written for conversational and AI interfaces and handed their team a prioritized set of findings. We are under NDA, so we can share our approach, just not the client or the product.
Confidential engagement · under NDA
Confidential · Global consumer e-commerce brand
Chat & AI assistant experience
Heuristic evaluation, conversational UX audit, AI usability review
What we can share
This is another project that stays private. A well-known consumer brand, with an online product used by millions of people, brought us in to review the chat and AI assistant experience woven through it, and asked us to keep the details confidential. What we can talk about is how we approached the review and what we handed back to their team. You will not see the company, its brand, or the actual product here. The image above stands in for them.
Why they called us
They had done something genuinely ambitious: rather than bolt a lone chatbot onto a support page, they had threaded a conversational assistant through the heart of a high-traffic product, so it could help people as they moved through the work they came to do. That is a hard thing to get right, and they knew it. Before investing further, they wanted an outside expert to read the whole experience honestly and tell them where it delivered and where it fell short.
Conversational and AI interfaces do not play by the same rules as an ordinary page. They set expectations with every reply, they can be confidently wrong, and they live or die on how gracefully they recover when a request lands outside what they can do. That is exactly the kind of nuanced, human read they wanted from us.
The heuristic evaluation
We evaluated the experience against established usability principles, extended with the heuristics that matter specifically for conversational and AI interfaces, always from the point of view of the person actually using it. We looked at how clearly the assistant signalled what it could and could not do, how visible its state was while it was thinking or acting, how it handled ambiguous or out-of-scope requests, how easy it was to correct or undo, and how it handed off to a person when that was the right move. Trust ran through all of it: whether the assistant set honest expectations, and how it behaved in the moments where a wrong answer would cost the user the most.
For each area we laid out the action items we would recommend, how much of a difference we expected each change to make, and what the experience would feel like for a real person working through it. Accessibility was part of that read too, since a conversational surface that is awkward with a keyboard or a screen reader quietly shuts people out. From there we gave them a straight recommendation on how to move forward, so the choices in front of them were clear rather than a pile of raw notes.
What they walked away with
They got a prioritized, plain-language set of findings ranked so the highest-impact fixes were obvious, framed for the people who own the product and build the assistant, alongside a clear read on what was already working well and worth protecting. For a confidential engagement like this one, what matters is that their own team could pick it up and act on it directly.