TjMakeBot puts data production, quality control, version tracking, training/export, and delivery acceptance on one workflow so autonomous-driving and embodied-AI teams can reduce tool switching and hand off results faster.
Start with self-serve 2D/3D Studio, move into AnnoClaw Workflow, or talk to us about Enterprise Gateway and private deployment.
Put video decoding, AI pre-labeling, human review, and training handoff in one workflow instead of across multiple systems.
Build a closed loop around 2D images, 3D point clouds, low-confidence fallback, human review, training/export, and delivery summaries.
Bring 2D images, 3D point clouds, video, multi-sensor assets, and automated inputs into one project workflow.
Turn review, issues, rework, SLA, and audit from isolated actions into an operable quality system.
Use spec versions, dataset versions, and releases to explain training lineage, quality rules, and delivery scope.
Make delivery summaries, artifacts, customer handoff pages, and audit trails into a result layer customers can buy and accept.
Let automation sessions return directly to project, review, training, and delivery workspaces so teams can reduce manual switching and repeated handoff.
When teams move from evaluation to real production, the platform must support permissions, private deployment, and enterprise procurement.
The value of this workflow is not one more AI feature, but connecting data production, quality control, version tracking, and delivery acceptance into one line.
Do not send images, point clouds, and automations into separate tools. Keep them inside one project context for review, training, and delivery.
AI can move first, but review, issues, rework, SLA, and spec versions determine whether delivery is trusted.
Once review is complete, the next steps should naturally become dataset versions, training/export, delivery summaries, and customer handoff pages.
The same workflow should cover project collaboration, quality gates, training/export, and customer handoff so teams do not rebuild the process every time they scale.
Keep uploads, automations, and human checkpoints on one operating path.
Turn review, issues, rework, and SLA into an operable quality system.
Use dataset and spec versions to explain where training and delivery came from.
End in delivery summaries, artifacts, and acceptance-ready handoff pages.
Whether a project focuses on multi-sensor perception, robotics task data, or customer acceptance, it still needs the same shared layer for data, quality, versioning, and delivery.
Organize multi-sensor perception data, low-confidence fallback, human review, version tracking, and delivery acceptance on one data-production chain.
Support more complex flows such as tasks, episodes, demonstrations, evaluation, and delivery so embodied-AI teams do not stop at one-off labeling.
Use OpenClaw to route automation back into project workspaces so workflow sessions can land in review, training, and delivery.
Support customer handoff pages, audit, permission boundaries, and private deployment so the platform can move from trial to procurement.
What lets a platform serve autonomous-driving and embodied-AI teams over time is not AI speed alone, but whether workflow, quality, version, and delivery all hold together.
AI can move first, but review, issues, rework, and SLA still decide whether delivery is trusted and purchasable.
Projects, review, evaluation, training, export, and handoff can stay in one workflow instead of being split across separate tools.
Once data production is done, teams can continue into training, export, delivery summaries, and customer handoff instead of ending with a raw download link.
When you move from trial into procurement, permissions, audit, and private deployment, you do not need to switch platforms.
Explore solutions, compare pricing, or email us for help with deployment, purchasing, and project delivery.
Use the AnnoClaw console for uploads, routing, human review, and automated delivery. Not ready to upload yet? Try the 2D / 3D quick experience first -> View all features ->
Have questions or need assistance? Reach out to our team.
Email us if you have questions about purchasing, deployment, datasets, or project delivery.
