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OCR extracts business names, formation dates, and addresses from uploaded documents automatically. But users still need to trust what the system found before anything gets submitted.
We designed the confirmation flow around editable fields that surface extracted data clearly. Users review what the system found and correct where needed, rather than typing everything from scratch. The design choice was to make the AI output feel transparent and controllable.
The design choice was to make the AI output feel transparent and controllable. The result is a flow that reduces data entry to the minimum while keeping users fully in control of what gets submitted.
Great work!
Editable-by-default beats a blind accept/reject toggle here — it keeps the correction cost at one field when OCR gets a formation date wrong, instead of forcing a full manual re-entry the moment trust breaks on a single value.
Confirmation flows for OCR-extracted data are so easy to get wrong, either too much friction re-verifying everything or too little and errors slip through. Did you test how far you could push toward minimal review before people stopped trusting the auto-fill entirely?
The editable-fields approach is smart, letting people verify OCR output instead of trusting it blindly probably saves you a ton of support tickets down the line.
The detail I would test next is per-field confidence: visually calm the high-certainty extractions and flag only the shaky ones, so review effort lands where the OCR actually struggles. I did this on a document-heavy onboarding flow and it cut correction time noticeably.
Looks good. I like the minimalist approach. Nice and clean.
This is great tho
Making AI output editable rather than just showing a result is such a smart trust-building move. Users feel in control instead of just hoping the system got it right.
The editable confirmation step does more than correct OCR errors. It creates an audit boundary between machine extraction and user intent. I’d surface confidence selectively by flagging uncertain fields instead of asking users to distrust every value equally. Strong product thinking.
Good work
Love this approach Making AI-generated data editable and transparent is such a smart UX decision. It reduces friction while still giving users full control. Really clean work!
The UX tension you've solved here is the right one. AI does the extraction but the user has to own what gets submitted, so locked read-only fields would create exactly the wrong feeling. Editable fields that surface the extracted value flip the dynamic from rubber-stamping to...
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Well designed, and it caught attention the first time
OCR extracts business names, formation dates, and addresses from uploaded documents automatically. But users still need to trust what the system found before anything gets submitted.
We designed the confirmation flow around editable fields that surface extracted data clearly....
Editable confirmation fields is such a smart UX call. Trust is the hardest thing to build in OCR flows and surfacing extracted data clearly solves that well.
While working on a website, I explored two different approaches for the header design. Same brand, same content, just a different feel and interaction style.
Option 1 - Classic
Option 2 - Modern
Would love your honest UX/UI take, which direction feels cleaner, more functional, and better suited for the overall experience, and why?
7 voted
23%
23 voted
77%
30 votes
Closed
Modern gives a really good feeling and make everything is clear
L’Empreinte — “The Imprint”
I wanted to explore the relationship we build with the objects around us — the marks they leave on us, and the marks we leave on them.
I started with François-Thomas Germain’s Coffeepot, made in Paris in 1757. What interested me was not only its form, but the world it represented: a time when rituals such as drinking coffee, serving and displaying silverware were surrounded by extraordinary care, craftsmanship and ceremony.
That made me think about our rituals today.
We listen through headphones. We gather around speakers. We work through laptops. We communicate through phones.
The objects have changed, but we still build habits, memories and relationships around them.
What if that same care, refinement and presence were applied to the products surrounding our lives today?
Rather than reproducing the coffeepot, I wanted its design language to leave an empreinte on a new generation of objects. Its flowing ornament, polished silver, sculptural curves and sense of preciousness became the basis for a collection of headphones, speakers, phones and laptops.
The aim was to imagine another evolution of modern design — one where ornament had never completely disappeared, and where technology could still feel crafted, emotional and precious.
Explore the full FLORA project here:
https://app.flora.ai/view/js7b1ngr9rm9d9v0cv3vzc6tkx8dvnrf
I’ve also included a process video showing how the idea developed from the original reference into the final collection and world.
Instead of placing the objects inside a cold technological setting, I built an environment around stone, natural textures, open space, light and the sky. The tree-like elements and blue-and-white opening above connect the space back to nature, contrasting with the polished technology and making the environment feel more grounded and alive.
The people complete that world. They are different, but their gestures overlap: listening, working, communicating and sharing sound. Their presence shows how these objects become part of the body, the room and the rituals that connect us.
That is ultimately what L’Empreinte represents:
the trace history leaves on design,
the trace objects leave on us,
and the trace we leave on them in return.
A collection imagining what might happen if the refinement of the past became part of the design language we still spoke today.
Created with @FLORA for the FLORA Museum Challenge.
Original work:
François-Thomas Germain, Coffeepot, Paris, 1757
The Metropolitan Museum of Art
Amazing approach and design work!