Afterframe

The operational problem

The client needed to use multiple image and video generation models without juggling separate tools, API keys, and inconsistent prompt workflows across ad hoc scripts and prototypes.

What we mapped first

1. Mapped which models the workflow actually used versus which were experimental noise.
2. Identified the handoff points between generation, review, and in-app editing.
3. Agreed which outputs needed human review before export or delivery.
4. Defined integration requirements for API keys, rate limits, and model failover behaviour.

What we built

A production-oriented web interface that unifies multiple image and video generation models behind one prompt workflow, with in-app editing and clear labelling of model outputs.

• Single interface for multiple generation providers with consistent prompt handling.
• Side-by-side or sequential comparison of model outputs for the same brief.
• In-app editing and refinement without exporting to unrelated tools.
• Configurable model routing so teams can add or retire providers without rebuilding the UI.

How it works

Web application with provider abstraction layer for image and video APIs.

Prompt templates and tuning controls shared across models.

Asset management for generated outputs with version history.

Integration patterns suitable for client-controlled API credentials.

Example use case

How a creative team can consolidate a fragmented AI production workflow.

Information handling and control

• API credentials stored according to deployment environment requirements.
• No training on client content unless explicitly agreed and documented.
• Clear audit trail of which model produced each asset.
• Export controls so only reviewed assets leave the workspace.

What the client receives

• Multi-model generation interface with unified prompt workflow
• Provider integration layer for image and video APIs
• In-app editing and comparison views
• Administrator configuration for models and credentials
• Handover documentation for operators and integrators

Product and system views

Unified prompt workflow across multiple generation models.

Compare outputs from different providers on the same brief.

Refine outputs without leaving the production interface.

What changed

• Teams evaluate models against real briefs instead of isolated API experiments.
• Prompt workflows become repeatable rather than reinvented per tool.
• Review and export paths are documented before content is delivered.
• The build demonstrates multi-vendor AI integration with clear operational boundaries.

What a similar project costs

Multi-model integration projects are scoped after discovery. A focused pilot over one or two providers is the usual starting point.

Indicative pilot pricing. Larger implementations are scoped separately following discovery.