Spatial Alignment Study
Aligned Across Views
Illustrative SequenceCustom Tooling / 3D Annotation
A new perspective on spatial annotation.
Extending CVAT with rotation-aware cuboids for more intuitive alignment across changing views.
Shaping structure for the world of AI.
Real-world complexity. Human understanding. The training data to take intelligence further.
Inside the data
The Data Layer
The world is unstructured. We make it learnable.
Every frame, every document, every point in space carries context. We keep it intact with precise annotation and human review, across five kinds of data.
Give your model a clearer view.
From object detection to full scene understanding, we keep boundaries, identities and relationships consistent as people and objects move through the frame.
Image & video workKeep the geometry. Keep the context.
Changing viewpoints demand spatial labels that agree with each other. We annotate synchronized views, cuboids, lanes and relationships, with careful human review on every sequence.
3D & spatial workStructure gives the words meaning.
OCR finds the words. Sections, tables, lists and hierarchy tell you how they relate. We validate that structural layer first, then extract the skills, clauses, dates and terms that sit on top of it.
Inside document intelligenceConnect what is said to what is happening.
Workflows for transcription, speaker identification and tasks that tie audio to other modalities, reviewed against the same rubric as everything else.
Audio & multimodal workA reliable dataset takes more than labels.
Bring the data you already have, or start with collection. We curate, clean, enrich and review it against a rubric you agreed to, and the review process ships with the dataset.
Data operations workThe Human Layer
Technology moves fast. Judgment moves it forward.
We build and manage the team behind your data. Trained for your task, calibrated on real examples, and supported by a process that improves with every review.
A Continuous Quality Loop
Follow the cycle or select a stageEvery review informs the next production cycle
Built around your problem.
Your taxonomy, risk, and delivery requirements shape the first pass. The team works to a shared rubric, using manual or model-assisted annotation where appropriate.
The Starting Point
- Task Rubric
- Domain Training
- Annotation Guidelines
Review the work that matters.
Check samples against the rubric, with attention to difficult cases and the errors that matter for your task. Bring uncertain examples into review.
Review Focus
- Label Consistency
- Task Requirements
- Edge Cases
Disagreement is useful evidence.
Group recurring differences so reviewers can distinguish an unclear rule from an annotation that needs correction. The pattern helps identify where guidance or coaching needs to improve.
Example Issue Types
- Occlusion
- Lane Boundaries
- Truncation
Make the reasoning clear.
Reviewers resolve ambiguous cases, correct labels, and document the reasoning so the team can apply the decision consistently.
Review Decisions
- Correct Labels
- Resolve Ambiguity
- Record Rationale
Make the decision repeatable.
Turn resolved cases into clearer guidelines, shared examples, and targeted coaching. Feed those changes into the next production cycle.
What Carries Forward
- Clearer Rubric
- Shared Examples
- Targeted Coaching
Your Turn
Draw the box. We'll score it like QA does.
Draw a box around the black pickup truck. We compare it with the reviewer's box using IoU: the area the two boxes share, divided by the area they cover together.
Arrows move · Shift+arrows resize · Enter scoresDrag corner to corner
Drag from one corner of the truck to the opposite corner. With a keyboard, press Enter to place a box, arrow keys to move it, Shift with arrow keys to resize it, Enter again to score and Escape to clear.
IoU with the reviewer's boxAccept 1.00
IoU with the reviewer's box: –
Only a perfect 1.00 is accepted into the dataset. 0.95 or higher is very close, 0.90 is close to precise, and anything lower goes to human review.
Build What Comes Next
The next breakthrough starts with better data.
Show us the data, the edge cases, or the gap. We’ll help shape the workflow that moves you forward.
Talk about your project