Editor blueprint
Start cutting with the right editor.
This page turns the matching logic into a profile-first interface. The search is live, the card is visual, and the experience is built around taste match instead of scrolling through a dead list of names.
Interactive prototype
The editor profile card, built as a live discovery surface.
This is the actual discovery interaction, not just the spec for it. Filters update the pool, the profile animates between candidates, and the card balances taste, logistics, and portfolio evidence across documentary, fashion, showreel, and experimental edit samples.
Editor profile
Zia Flux
aka Zoya Khan
27 / Female / Kolkata
Raw, handheld, social-first, mixed-media.
Taste match
Variable BPM · EclecticHardware badge
iPad Pro + MacBook Air / hyper-mobile edit workflowTech mode
On-Site MobilePricing
INR 1,200/minWhy this editor
She is the new-talent discovery. Her work breaks format rules and is strongest when the brief wants raw social texture.
Core logic
Landscape textural pacing — fills the frame edge-to-edge with observational energy and raw mixed-media texture.
Discovery bar
The top filter bar should feel alive, not form-like.
The core job of the top navigation is to keep the editor card reacting to taste, rhythm, production mode, and city with almost no delay.
Aesthetic vibe
Dropdown or vector-driven prompt
Choose signals like "Gritty," "Neon," "Minimalist," or "Golden Hour."
Rhythm
Slider or genre toggle
Move between slow-burn documentary pacing and hyper-edited commercial tempo.
Tech spec
Toggle filter
Switch between "Fixed Workstation" and "On-Site Mobile" depending on the production need.
Location
City search
Filter around hubs like Mumbai, New Delhi, Bengaluru, and Kolkata in real time.
UX flow
The motion logic should make the interface feel like a supercut.
The interaction is simple on purpose: one card, one clear next action, and a loop that always reflects the live filtered pool.
The Next loop
A floating "Next Editor" action cycles only through the currently matching pool.
Instant filter logic
As the search changes, the eligible list updates immediately and the loop narrows with it.
Taste match score
Every profile carries a visible percentage that translates vector closeness into a simple decision signal.
Developer handover
Build this like a fast SPA, even when the content feels cinematic.
Use Framer Motion for card transitions, keep videos lazy and muted, and let a single Supabase query combine vector similarity with relational filters so the surface keeps the 200ms discovery feel.
Implementation note
The profile data should be fetched through one query path that handles city, hardware, pricing logic, and taste vectors together instead of splitting the search across disconnected layers.