Beta

Image Studio AI

mage Studio AI is a B2B food visual generation tool built under SideChef.

As the sole designer, I led the 0→1 design — replacing unpredictable, prompt-heavy AI workflows with structured visual controls that deliver brand-consistent outputs at scale.

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Client:

SideChef (B2B SaaS)

Role:

Lead Product Designer (UX + UI + Storytelling)

Scope:

End-to-end design (0 → 1), workflow, UI system, landing page

End-to-end design (0 → 1), workflow, UI system

Timeline:

6–8 weeks

Team:

PM + Design (me) + Engineers

THE CHALLENGE

Two broken paths, zero good options.

Food brands produce hundreds of visual assets monthly. They have two options — and neither works:

Two broken paths, zero good options.

Food brands produce hundreds of visual assets monthly. They have two options — and neither works:

Traditional Photoshoots

Generic AI Tools

Speed

Days to weeks

Minutes

Cost

$2K–$10K+ per shoot

Low

Quality

High

High (individual)

Brand consistency

Controlled

Random per generation

Controllability

Full

Prompt-dependent

Scalability

Linear cost

Inconsistent at volume

The gap

Brands need AI speed and cost — with photoshoot-level consistency and control.

DISCOVERY

35 interviews. 4 insights that shaped every design decision.

Observed end-to-end workflows from “brief → assets → publishing"
Reviewed common failure cases in AI-generated food visuals (unrealistic texture, lighting mismatch, composition drift)

Method

Traditional Photoshoots

Generic AI Tools

Contextual observation

8 sessions

Iteration takes 3–5× longer than generation

AI output audit

200+ images

Consistency fails across sets, not within single images

Semi-structured interviews

35 people

Four recurring themes ↓

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Problem Statement

Food brands and creators need to generate large volumes of high-quality food visuals, but existing workflows are expensive and slow, and generic AI tools create inconsistent results that require heavy prompting and rework.

Design Goals × Metrics

How We Measured
Success

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

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.

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Avg. Total Usage Time / User

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Avg. Total Usage Time / User

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Avg. Total Usage Time / User

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Avg. Images Generated / User

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Avg. Images Generated / User

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Avg. Images Generated / User

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NPS Score

(Users rate the product as easy and valuable to use.)

NPS Score

(Users rate the product as easy and valuable to use.)

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MAU Growth

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MAU Growth

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MAU Growth

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Avg. Sessions / User

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Avg. Sessions / User

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Avg. Sessions / User

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Beta

Image Studio AI

mage Studio AI is a B2B food visual generation tool built under SideChef. As the sole designer, I led the 0→1 design — replacing unpredictable, prompt-heavy AI workflows with structured visual controls that deliver brand-consistent outputs at scale.

Client:

SideChef (B2B SaaS)

Role:

Lead Product Designer (UX + UI + Storytelling)

Scope:

End-to-end design (0 → 1), workflow, UI system, landing page

End-to-end design (0 → 1), workflow, UI system

End-to-end design (0 → 1), workflow, UI system

Timeline:

6–8 weeks

Team:

PM + Design (me) + Engineers

THE CHALLENGE

Two broken paths, zero good options.

Food brands produce hundreds of visual assets monthly. They have two options — and neither works:

Two broken paths, zero good options.

Food brands produce hundreds of visual assets monthly. They have two options — and neither works:

Speed

Days to weeks

Minutes

Cost

$2K–$10K+ per shoot

Low

Quality

High

High (individual)

Brand consistency

Controlled

Random per generation

Controllability

Full

Prompt-dependent

Scalability

Linear cost

Inconsistent at volume

The gap

Brands need AI speed and cost — with photoshoot-level consistency and control.

DISCOVERY

35 interviews. 4 insights that shaped every design decision.

Observed end-to-end workflows from “brief → assets → publishing"

Reviewed common failure cases in AI-generated food visuals (unrealistic texture, lighting mismatch, composition drift)

Contextual observation

8 sessions

Iteration takes 3–5× longer than generation

AI output audit

200+ images

Consistency fails across sets, not within single images

Semi-structured interviews

35 people

Four recurring themes ↓

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

mage Studio AI is a B2B food visual generation tool built under SideChef.

As the sole designer, I led the 0→1 design — replacing unpredictable, prompt-heavy AI workflows with structured visual controls that deliver brand-consistent outputs at scale.

How We Measured
Success

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.

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.