Industrial vision teams
Standardize annotation, review, and delivery expectations instead of relying on manual coordination.
Data workflow
Move from annotation tasks to a reusable loop across review, training, export, and delivery
TjMakeBot keeps project intake, AI pre-labeling, human review, training handoff, export, and delivery summaries in one production loop instead of scattered tools.
AI pre-labelingReview checkpointsTraining and export handoffDelivery summary
What this workflow includes
Use one connected workflow to organize data intake, labeling rules, review, training, export, and delivery handoff.
Keep uploads, project intake, and labeling rules on one operational track
Move AI pre-labeling directly into human review and rework
Share the same project context across training, export, and delivery summaries
Show each team member what stage the project is in and what happens next
Feed accepted outcomes into the next version cycle instead of rebuilding the process each time
Best fit
Robotics and edge-AI teams
Keep data production and training handoff stable as scenarios and sensors evolve.
Outsourced or multi-role teams
Explain the current stage, owner, and next step to customers and project participants on the same page.
Projects that need acceptance loops
Deliver more than files by pairing outputs with version, summary, and next-step context.
Typical closed-loop flow
See the main production stages from project kickoff to delivery acceptance, with each step linked to the next action.
01
Project intake and data onboarding
Set goals, label rules, and delivery requirements before data enters the project.
02
AI pre-labeling with human review
Let AI cover first-pass work, then stabilize quality through review and rework.
03
Training, export, and delivery summary
Connect approved data to training and export, then present outcomes through a shareable delivery surface.
04
Acceptance feedback into the next version
Carry acceptance feedback and edge cases back into the next iteration instead of restarting from zero.
What teams gain
Fewer handoff gaps between upload, review, training, export, and delivery
Keep AI entry points and human quality checkpoints inside the same product surface
Make every delivery page carry version, download, and recap context by default
Turn one-off projects into reusable templates and operating patterns
Data workflow
Run the full data loop from one workflow entry
Use Workflow Cloud to connect intake, annotation, review, training, export, and delivery in one operating path.