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ELEVZE Labs
AI & Agents2026IN-DEVELOPMENT

AI Product Imagery Pipeline for Ecommerce

From one approved packshot to a reviewable family of ecommerce-ready product scenes.

One ecommerce skincare bottle progressing from a source packshot into three styled campaign scenes

Overview

ROLE
AI product design and engineering
SCOPE
Product image intake, scene generation, automated quality gates, human review and channel-ready export.
01 / The problem

Every new campaign image restarted the production process.

Ecommerce teams need a growing set of product images for product pages, marketplaces, social campaigns and seasonal launches. Traditional production makes each new background, crop or format another brief, handoff and review cycle.

Uncontrolled image generation is not a replacement: a visually appealing result is unusable when the product shape, packaging details or proportions drift. The real problem is building repeatability and approval into the workflow.

02 / The solution

A generation pipeline with product identity and review at its centre.

The pipeline begins with an approved source packshot and a structured scene brief. It normalizes the source, generates a controlled set of scene variants, checks the outputs for product and format consistency, and routes the strongest candidates into a human review queue.

Approved images are exported in the aspect ratios and resolutions required by each ecommerce channel. Rejected results retain their feedback so the next generation pass starts with better constraints instead of starting over.

  • 01Approved source-image intake
  • 02Reusable scene and channel presets
  • 03Batch generation with controlled variation
  • 04Product-shape and composition quality gates
  • 05Human approval before publishing
  • 06Channel-specific crops and exports
03 / Engineering

How the system is put together.

  1. L01

    Source intake

    Approved packshots and product constraints enter the workflow.

  2. L02

    Normalization

    Background, framing and product metadata are prepared.

  3. L03

    Generation

    Scene variants are produced from reusable creative presets.

  4. L04

    Quality gate

    Vision checks flag identity, crop and artifact problems.

  5. L05

    Human review

    A reviewer approves, rejects or requests another pass.

  6. L06

    Delivery

    Approved images are exported for each commerce channel.

04 / Technology

What it is built with.

FRONTEND
  • Review workspace
BACKEND
  • Workflow orchestration
  • Queue workers
DATA
  • Object storage
  • Job metadata
AI
  • Generative image models
  • Vision quality checks
INFRASTRUCTURE
  • GPU inference
  • Managed deployment
05 / Technical detail

Decisions worth recording.

Product identity first

The source packshot remains the reference throughout generation. Outputs that alter the silhouette, proportions or defining package details are held back from review.

Presets, not free-form prompts

Reusable scene and channel presets turn creative direction into repeatable inputs while still allowing controlled variation between outputs.

Human-in-the-loop publishing

Automated checks reduce review noise, but no generated image is treated as publishable until a person approves it.

Traceable delivery

Each export retains its source, preset, generation pass and approval state so teams can reproduce or revise an asset later.

06 / Outcome

What changed.

The pipeline turns product imagery into a managed content operation rather than a sequence of isolated generation requests.

Teams can explore more campaign directions from one approved source while keeping product consistency, review responsibility and final delivery visible throughout the process.

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