Service
AI features fail on interface far more often than on model quality. The design work is in confidence, correction, sourcing and expectation-setting — making a probabilistic system feel trustworthy without overclaiming what it can do.
Is this you?
If none of these sound like your situation, this is probably not the right service — and I would rather point you somewhere useful than sell you the wrong thing.
What happens
An interaction model that fits the feature — chat is rarely the right answer.
Confidence states designed as first-class UI, so uncertainty is visible rather than hidden.
Sourcing and citation patterns that let users verify an answer in one tap.
Correction and feedback flows that turn a wrong answer into a fast fix.
Onboarding that teaches capability and limits before the first failure.
Streaming, stopped, retried, rate-limited and failed states, all designed.
Editable source files you own outright, plus the documentation that makes them usable by someone who was not in the room.
3–6 weeks
That range assumes feedback comes back within two working days. Review latency is the single biggest cause of slipped design timelines — more than scope, more than complexity.
Get a fixed quoteProof
Point, scan, decide — in three seconds
A barcode and label scanner that tells people whether a product fits their diet. I designed the scan-to-answer loop, the confidence and correction states, and the onboarding that teaches the app's limits honestly.
An AI assistant that shows its work
A workplace AI assistant. I designed the conversation surface, the source-citation pattern and the workspace where teams organise prompts, so answers can always be traced back to where they came from.
Tell me where the product is today and what is going wrong. You will get a straight answer on scope, timeline and price.
madnansakhi@gmail.com