Coverage
2D / 3D / Video
Images, point clouds, frame review, and human checkpoints can share one workflow.
OpenClaw showcase
AnnoClaw Workflow / OpenClaw: an annotation workbench for review, training, export, and delivery in one loop
AnnoClaw Workflow is one of TjMakeBot's clearest differentiators. This page is not only about compatibility. It turns 2D/3D annotation, human review, training/export, delivery summaries, and OpenClaw-compatible into one workflow story teams can evaluate, demo, and operate.
Annotation WorkbenchHuman Review Checkpoint2D / 3D / VideoTraining + ExportDelivery Summary
Use this page to decide whether OpenClaw is the right entry for your current workflow, team handoff, and delivery path.
Launch path
3-step setup
Start with the wizard, move into the workbench, then connect training/export and delivery.
Delivery output
Summary + Files
The flow ends in an acceptance-ready result page, not just a task run.
Integration pack
5 assets
Manifest, skill, template, compatibility, and smoke-test resources are ready to use.
Why teams use OpenClaw
Start here if you need to understand what OpenClaw changes in the real operating path, not just which features exist.
Core difference
Not an isolated labeling page, but a workbench from data to delivery
AnnoClaw Workflow keeps annotation, review, training, export, and delivery connected so the team can stay on one operating path.
Teams do not need to bounce between multiple systems to finish a project.
Managers can judge progress around delivery readiness, not just task status.
Workflow sessions can point back to the collab project, review queue, training page, and delivery workspace.
Engineering teams can still keep the OpenClaw compatibility path.
Human review
AI moves first while humans keep the final gate
Automation removes repetitive steps, but critical checkpoints return to the main editor so quality ownership stays explicit.
Best for teams that care about quality, acceptance, and customer delivery.
Review is a core workflow node, not a side patch.
It becomes easier to explain where credits and human effort were spent.
Coverage
One public path for 2D, 3D point cloud, and video-frame operations
Use this section to see whether OpenClaw fits the way your team handles image, point-cloud, or frame-review work today.
2D datasets fit batch review and standardized export.
3D point clouds fit multi-view QA and pre-training inspection.
Video-frame workflows fit frame sampling, review, and traceable delivery.
Launch speed
Wizard, workbench, and technical assets are available together
Start with the wizard to validate the path quickly, then go deeper with the manifest, skill pack, and templates when integration is needed.
Validate quickly before committing to deeper integration.
Technical resources and product entry points are no longer scattered.
This fits evaluation, POC, and production rollout in parallel.
Outcome
Training, export, and delivery summary stay on one result path
The page must answer what teams get at the end, not just what features exist. The emphasis is on training metrics, export files, version context, and delivery summaries.
A stronger story for demos, POCs, and enterprise acceptance.
Less friction from scattered outputs after tasks finish.
A cleaner bridge into Pricing and Tutorials.
Where it fits
Use the image, point-cloud, video, and team-handoff scenarios below to see whether the workflow matches your way of working.
Use case
2D image operations
Best for teams that need annotation, sampling review, batch fixes, and export on one delivery rhythm.
Image tasks keep growing
Human QA and acceptance matter
You do not want training/export moved into another system
Outcome: It upgrades “labeled” into “reviewable, exportable, and delivery-ready.”
Use case
3D point cloud and robotics data
Best for workflows that need stable coordination between multi-view checking, point-cloud labeling, and pre-training QA.
Point-cloud work is complex and review-heavy
Multi-view quality confirmation is required
Labels often return before training
Outcome: 3D data moves from editor work into training, export, and result summaries on one path.
Use case
Video frames and sequence review
Best for frame sampling, timeline review, and staged delivery workflows rather than a one-off export step.
Frame volume is high
Deliveries happen in stages
Ops and reviewers need spend visibility
Outcome: Sequence data gets a review-to-delivery path teams can actually track.
Use case
Team operations and customer handoff
Best for organizations that want operations, review, training, and acceptance on one narrative instead of scattered links.
Customers expect acceptance summaries
Roles are split across teams
Every spend event should map to a business action
Outcome: OpenClaw becomes a project operating surface, not just an engineering connector.
How OpenClaw connects back into the team workspace
OpenClaw should not leave automation isolated. It should route work back into the project, review, training, and delivery pages the team already uses.
Project workspace
See current blockers, versions, specs, and release readiness in one place.
Next step ->Review queue
Route human checkpoints into one review queue with issue tracking and SLA visibility.
Next step ->Training workspace
Keep dataset lineage, release source, metrics, and exports connected to the same workflow run.
Next step ->Delivery workspace
Publish delivery summaries, artifacts, customer share pages, and audit trails without leaving the main platform.
Next step ->Start in 3 steps
If you are ready to try it, these 3 steps are the fastest way to start.
01
Use the wizard to validate the shortest launch path first
Confirm the gateway, templates, and site entry all work before deciding whether you need lightweight validation or deeper integration.
Validate the base connection
Generate the recommended entry path
Reduce first-setup friction
02
Use the workbench to drive human review and stage progression
The workbench keeps stage state, human confirmation, and next actions together, which is better for real workflow validation than isolated API calls.
See workflow stages
Complete human review
Keep the quality gate intact
03
Land training, export, and delivery results on an acceptance-ready page
The final output is not just a file. It is a result page with training metrics, version context, downloads, and delivery notes.
Training metrics
Export files
Version and delivery summary
What you receive at the end
A delivery summary page that customers and internal teams can review together.
Training metrics and version context so model outputs are easier to explain.
Download entries that move straight from the result page into downstream work.
Human-review trace points that clarify where automation stopped and people approved.
OpenClaw-compatible resources for teams that still need migration or debugging support.
A smoother path into tutorials, pricing, and solution pages.
When this path fits best
Why many teams start with this workflow path
What matters is whether OpenClaw helps your team keep automation, review, training/export, and delivery on one continuous path.
+Best for teams that need to explain spend and delivery readiness to customers, managers, or procurement.
+Best for mixed 2D / 3D / video production rather than a single isolated labeling screen.
+Best for SaaS workflows that need review, training/export, and project handoff to read as one loop.
Technical resources
If you are ready to go deeper into integration, debugging, or migration, start with the resources below.
Machine-readable
Agent Tool Manifest
Lets OpenClaw or other agents auto-discover TjMakeBot workflow, human-review, training/export, and delivery capabilities.
Decision policy
OpenClaw Compatibility Skill Pack
Gives engineering teams reusable calling policy, review boundaries, and delivery decision logic.
Recommended path
Agent Workflow Template
Recommended workflow template for integration teams that need to stand up annotate-review-train-export quickly.
Migration / debugging
Compatibility Template
Useful for legacy migration and compatibility debugging, but not recommended as the primary public path.
Shortest validation
Smoke Test Template
Useful for shortest-path gateway validation, but not ideal as the long-term workflow entry.
In hosted mode, call `/api/openclaw-gateway` only. Do not expose upstream service addresses in browser nodes.
Do not place App-Id, Salt, Sign, or `apiSecretKey` in public JSON, templates, or client-side scripts.
For production, prefer workflow sessions, review handoff, and delivery summaries as the primary path instead of relying on smoke tests.
FAQ
Who is this page for?
It is designed for people who want both a clear workflow overview and a deeper path into integration. The first half helps with fit, while the resource section helps with setup.
What is the biggest difference from a normal annotation-tool page?
The emphasis is not the isolated labeling action. It is the workflow story that keeps review, training/export, delivery summary, and result traceability together.
Where should teams start if they only want the shortest validation path?
Start with the Config Wizard. It is the fastest first checkpoint. Once the basics work, move into the workbench to validate the full stage flow.
Are the technical resources meant for every visitor?
No. Most visitors only need the workflow overview, use cases, and launch steps first. The resource area is better for deeper setup, debugging, or migration work.
Next step
Run the path once, then decide whether to integrate deeper or move straight into team evaluation
If you are still checking fit, continue with the use-case page and tutorials. If you are ready to connect it, go straight into the wizard, workbench, and the technical resources above.