Annotation, spatial labeling, quality assurance, automation and AI engineering — delivered by one team, under one set of guidelines, with the model objective defined before the first label.
Seven capability areas, delivered by one team under one set of guidelines. Hover a card to see what it covers.
Structured, guideline-compliant labels across image, video and text at production scale.
Spatial labeling for autonomous systems and robotics, from point clouds to tracked 3D objects.
Vision, language, audio and document data prepared for a single model objective.
Quality designed into the workflow: audits, metrics, root-cause analysis and corrective loops.
Repetitive data operations replaced with reliable, monitored pipelines.
Retrieval systems and internal AI tooling built around your own documents and data.
Audio, text and documents converted into clean, structured, verifiable records.
Every dataset we deliver moves through the same five stages. Nothing is labeled without a guideline, and nothing ships without validation.
Raw images, video, audio, documents and 3D data
Precise human-generated labels and structured datasets
Multi-level quality control and guideline compliance
Python, AI and workflow automation
Production-ready datasets for AI systems
One source frame, four output states. Switch between raw footage, 2D annotation, segmentation masks and 3D cuboids, filter what you want to look at, and inspect a single detection. This is a demonstration built for this page — not client work or real detection results.
2D boxes, class and confidence
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Detection output
Values shown are illustrative. Real projects define their own classes, confidence thresholds and acceptance criteria before annotation begins.
Inspector
PERSON · T-014
Illustrative visuals of the four data types we work with most. These are reference images created for this page — not client deliverables.
3D / LiDARPoint cloud labeling
Spatial scans labeled with 3D boxes, class and track identity for autonomous and robotics models.
AnnotationImage & video annotation
Bounding boxes, polygons, segmentation and keypoints produced against a defined label schema.
AutomationPipelines that report on themselves
Python and script-driven flows replace repetitive file handling, checks and reporting.
TranscriptionAudio to structured records
Speech, text and documents converted into clean, verifiable, structured output.
Tell us what you're building. We'll help you design the data workflow behind it.
iamayyubansari@gmail.com