Automation case studyAnonymized client project

One production workflow for image, video, prompt and document generation

Imagen coordinates several generative AI workflows behind one application. Users can prepare prompts, start media jobs, follow progress and retrieve results without working directly with each provider or backend process.

Imagen | AI Media Generation Platform main project interface

01 / The problem

What the team needed to fix

Media generation jobs can take time, fail at different stages and return files in different formats. A production product needs more than an API call. It needs job state, retries, storage, progress updates and a clear record of the requested output.

The platform also had to support several generation types without coupling the entire application to one model provider.

02 / The build

How I approached the system

I developed a Python backend that organizes image, video, prompt and document generation as multi-stage jobs. The workflows can call different AI services, process returned media and store the final assets in cloud storage.

Asynchronous orchestration keeps long-running work outside the request cycle, while WebSocket updates show progress and completion inside the product interface.

03 / Capabilities

What the product does

Multi-stage generation

A single request can move through prompt preparation, provider calls and media processing.

Asynchronous jobs

Long-running generation does not block the main application request.

Realtime progress

Users receive state changes while the backend continues processing.

Prompt intelligence

Prompt workflows prepare and refine inputs for the selected output type.

Media processing

Returned files can be validated, transformed and prepared for delivery.

Cloud storage

Generated assets remain available after the job has completed.

04 / Workflow

How the workflow moves

01
Create the request
The application records the desired asset, input material and generation options.
02
Prepare the workflow
Prompt and provider steps are selected for that output type.
03
Process asynchronously
The backend runs generation and media tasks while publishing progress.
04
Store and deliver
The finished asset is saved and returned through the application.

05 / Product screens

Inside the project

Select a screen to inspect the interface, workflow and operational details more closely.

Product walkthrough

01 / 09

Imagen | AI Media Generation Platform product view 1

06 / What changed

The practical result

Different media workflows share a common job and progress model.
Long-running generation remains responsive from the user's point of view.
Provider-specific logic stays behind the backend instead of spreading through the product interface.

Common questions

Useful context before a similar build

The work usually includes model integrations, prompt pipelines, background jobs, progress tracking, media processing, storage and the product interface around those systems.

Image and video generation may take longer than a normal web request. Background jobs improve reliability and make retries and progress updates easier to manage.

Yes. A provider abstraction can keep workflow and product logic stable while individual generation services change.

The job system records each stage so failed tasks can be inspected and retried without asking the user to restart the entire workflow.

Generated files are stored outside the application server and linked to their job record so delivery and history remain reliable.

Yes. Realtime events can publish job status changes to the interface while longer image or video tasks continue in the background.

Work with me

Building an AI media product?

I can help design the backend workflows, realtime product experience and provider integrations needed beyond the first prototype.

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One production workflow for image, video, prompt and document generation

What the team needed to fix

How I approached the system