Image Studio AI
Productizing Generative AI for Production
I led the end-to-end system design, building a structured control layer on top of a probabilistic model.
The introduction of controlled generation workflows reduced iteration friction and improved enterprise trust post-launch.
Impact
+0%
MAU Growth (MoM)
0
images/user
DISCOVERY
My Approach: Build Control Around the Model
Instead of:
Prompt → Generate → Download
Problem Statement
Design Goals × Metrics
How We Measured
Success
1
Reduce production time
Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations
2
Improve brand consistency
Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set
3
Make AI usable for non-experts
Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts
4
Enable scalable production
Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack
Solution (Workflow-First AI Studio)
Replace prompt-heavy input with structured visual controls
Instead of relying on free-text prompts, we exposed key food photography variables as explicit UI controls,
These variables are what actually drive visual consistency. Making them explicit reduces ambiguity and lowers the learning curve for non-expert users.
Enable user-created presets through reusable settings
Rather than providing system-defined presets, the beta allows users to turn a successful configuration into a reusable baseline via “Use Settings”.
Brand teams care about repeating their definition of “good”. User-created presets preserve consistency without enforcing assumptions too early.
Threaded history with one-click reuse & regenerate
The generation history is designed not only for review, but for fast reuse and regeneration.
In creative workflows, restarting is expensive.
By allowing users to reuse and regenerate directly from history, we turn past success into the fastest path forward.
Mode-based workflow switching
Users can switch modes instantly without leaving the page or losing context.
In real production workflows, teams move fluidly between generating, refining, and branding assets. Mode-based switching reduces friction and prevents users from restarting tasks in separate tools.
Integrated Product Placement for brand-ready visuals
Users can insert branded products into generated visuals without leaving the generation context, instead of exporting images to external editing tools.
Brand and commerce teams often need visuals that include specific products.
Embedding product placement into the same workflow reduces handoff friction and shortens the path from generation to publishable assets.
Before / After comparison (Comingsoon)
To help users evaluate AI outputs more efficiently, we introduced before / after comparison between the reference image and generated results.
ComingSoon
In creative workflows, decision-making is often the slowest step. Side-by-side comparison reduces cognitive load and shortens iteration cycles by making differences immediately visible.
Smart Style Guideline Generation from Uploaded References
To help brand teams scale visual consistency more efficiently, we explored a solution that allows users to upload existing photo guidelines (images or PDFs), which the system can then translate into structured, usable style guidelines.
ComingSoon
Many brands already have established visual standards. Translating existing guidelines into machine-readable controls significantly reduces setup cost and accelerates adoption for enterprise users.
Results & Impact
Deeper User Engagement
These signals suggest that guided controls, reusable settings, and integrated workflows effectively reduce iteration cost and support real-world content production.
