Who should use the Inference workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for inference with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A finalized final deliverable is ready for publishing, handoff, or integration.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A finalized final deliverable is ready for publishing, handoff, or integration.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Tenstorrent to inputs, context, and settings are ready so the workflow can move into execution without blockers. Then, you pass the output to Simplified AI Image Generator to supporting assets from text-to-image are prepared and connected to the main workflow. Then, you pass the output to Places365 to supporting assets from semantic segmentation are prepared and connected to the main workflow. Then, you pass the output to Together AI to a first-pass final deliverable is generated and ready for refinement in the next steps. Then, you pass the output to Reface to the final deliverable is improved, validated, and prepared for final delivery. Then, you pass the output to Lensa AI to the final deliverable is improved, validated, and prepared for final delivery. Finally, Candis is used to a finalized final deliverable is ready for publishing, handoff, or integration.
AI Model Inference
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Text-to-Image
Supporting assets from text-to-image are prepared and connected to the main workflow.
Semantic Segmentation
Supporting assets from semantic segmentation are prepared and connected to the main workflow.
Inference
A first-pass final deliverable is generated and ready for refinement in the next steps.
Face Swapping
The final deliverable is improved, validated, and prepared for final delivery.
Background Replacement
The final deliverable is improved, validated, and prepared for final delivery.
OCR
A finalized final deliverable is ready for publishing, handoff, or integration.
Prepare inputs and settings through AI Model Inference before running inference.
AI Model Inference sets up the foundation for inference; clean inputs here reduce downstream rework.
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Use Text-to-Image to build supporting assets that improve inference quality.
Text-to-Image strengthens inference by feeding better supporting material into the pipeline.
Supporting assets from text-to-image are prepared and connected to the main workflow.
Use Semantic Segmentation to build supporting assets that improve inference quality.
Semantic Segmentation strengthens inference by feeding better supporting material into the pipeline.
Supporting assets from semantic segmentation are prepared and connected to the main workflow.
Execute inference with Inference to produce the primary final deliverable.
This is the core step where inference actually happens, so it determines baseline quality for everything after it.
A first-pass final deliverable is generated and ready for refinement in the next steps.
Refine and validate inference output using Face Swapping before final delivery.
Face Swapping adds quality control so issues are caught before the workflow is finalized.
The final deliverable is improved, validated, and prepared for final delivery.
Refine and validate inference output using Background Replacement before final delivery.
Background Replacement adds quality control so issues are caught before the workflow is finalized.
The final deliverable is improved, validated, and prepared for final delivery.
Package and ship the output through OCR so inference reaches end users.
OCR is what turns intermediate output into a usable, publishable result for real users.
A finalized final deliverable is ready for publishing, handoff, or integration.
§ Before you start
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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