AI demos without product logic
Many starter kits stop at provider invocation and leave queue control, billing, and admin handling undefined.
Feature / AI Workflow
AI workflow that connects product UX, provider execution, queue control, and billing
BunShip is not only an AI demo shell. It includes a productized workflow for submitting tasks, controlling queue behavior, pricing usage, and exposing results through reusable interfaces.
- Image-generation workflow already live
- Multi-provider model story
- Queue-aware execution path
- Works with credits, refunds, and admin controls
1 loop
Product workflow
Submit, govern, price, and expose AI work through one connected path.
4+
AI surfaces
Provider config, prompt controls, queue handling, and history fit together.
Ops-ready
Failure model
Timeouts, retries, cancellation, and compensation are part of the story.
The AI workflow page should demonstrate that BunShip covers more than a prompt form and result gallery.
Prompt workflowProvider controlsTask historyBilling-aware generation
What this page solves
AI builders need proof that the starter can survive real usage and operator pressure, not just render a single model demo.
No path from prompt to monetization
An AI workflow must connect usage, credits, and user-visible history to become a product.
No safety story for provider failures
Retries, cancellation, and timeouts should be visible before the buyer opens the codebase.
What's included
This page should own the broad AI app intent while queue governance stays as a narrower supporting page.
AI image workflow
A live generation workflow already exists and can be adapted to your own AI product domain.
Provider-aware execution
The product story includes model provider configuration instead of assuming one hardcoded vendor.
Usage and pricing integration
Credits and pricing logic are part of the workflow rather than a separate afterthought.
Why it matters
This is one of BunShip's clearest differentiators because it connects AI product ambition to operating reality.
Raises AI-specific conversion quality
Buyers see that BunShip is designed for AI product execution, not just generic SaaS.
Supports clearer AI search exposure
The page can answer module-level AI questions cleanly for search engines and answer engines.
Bridges product and ops
Queue, pricing, provider control, and UI output are described as one system.
Implementation notes
These links separate the main AI workflow story from its deeper implementation surfaces.
AI image workflow docs
Review the implemented image generation flow and where it can be customized.
/docs/features/ai-image-workflow
AI providers docs
See the provider layer and model integration surfaces.
/docs/features/ai-providers
Queue governance page
Drill into retries, throughput control, and job safety without overloading this page.
/features/queue-governance
Who it's for
Best for teams choosing a starter because they need AI-native workflows, not just a generic app shell.
AI app founders
Use the existing generation loop as the base for text, image, or other task-driven products.
Internal AI tool teams
Ship governed AI task execution without rebuilding admin, billing, and queue surfaces.
Agency AI builds
Start from a workflow that already explains provider choice, queue handling, and usage economics.
FAQ
Keep this page focused on the broader workflow so queue-specific questions can be routed cleanly.
Next step
Use the AI workflow page as the main proof of BunShip's AI-native product direction
Open pricing if you are evaluating the product commercially or read the workflow docs if you need implementation depth.