Collect. Annotate. Validate. Build Better AI
Manipulation, imitation-learning, vision-language (VLM), and navigation models break down on cluttered scenes, shifting grasps, and unclear task boundaries. We annotate images, video, and 3D data through a multi-stage QA process, so your model trains on labels it can trust.
The accuracy of your labels is the accuracy of your AI model, and our process is built around that.
Robots learn from what annotators mark. Objects overlap on a table, a grasp changes within a few frames, and the start and end of an action are easy to place differently. Human and robot segmentation must stay consistent so that interaction models do not confuse the two.
Inconsistent labels become failed grasps in manipulation models, weak imitation learning, vision-language models that misread what a robot is doing, and navigation that stalls in real environments.
We label the data these models depend on, including hand pose, hand-object interaction, grasp type, procedural steps, object state changes, 3D cuboids, camera pose, and captions and question-answer pairs for VLMs. Annotators are trained on your object classes, action definitions, and temporal rules before production begins. Every batch passes internal and external quality checks, and errors are corrected at the source.
Our object detection annotation and 2D bounding box annotation label tools label parts, containers, people, and obstacles in robot camera images and video. This precisely annotated data helps robots recognize what is in front of them and where it sits.
Our video annotation services tag robot and human actions with start and end times, and divide long recordings into labeled steps. These labels train models to recognize what a robot is doing at each moment of a task.
We annotate demonstration videos by labeling objects, hand and tool positions, and each stage of the task, using key point annotation where body or hand pose matters. This annotation for machine learning gives imitation learning models clear examples of how a job should be done.
Annotators use bounding boxes and polygons to label parts, tools, and fixtures on a tabletop, then tag the order in which parts are picked and assembled. These labels help models learn correct sequencing and spot missing or misplaced components.
Our semantic segmentation services separate people, robots, and background pixel by pixel, including when they overlap or partly hide each other. This clean separation helps models keep track of who and what is in a shared workspace.
Our 3D data annotation places 3D bounding boxes and cuboids around objects in point clouds and depth data, with size, position, and orientation. These labels give robots the spatial understanding needed to move and handle items safely.
We label objects, grasp points, grasp types, and the moment of release across images and video. This AI annotation trains models to choose a secure grip and complete pick-and-place tasks reliably.
Annotators label floors, walls, doors, obstacles, people, and clear routes, and mark traversable paths in camera and 3D data. These labels train navigation models to plan routes and avoid hazards.
We annotate videos of people working near robots by labeling gestures, body pose, handovers, and proximity events. This data annotation for AI helps models respond safely and predictably to human behavior.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Robotics data annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For robotics, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, robotics projects may involve robot-view images and video, objects, grasp targets, human actions, scene elements, and task events when the project defines those labels. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include First-person viewpoints, motion blur, occlusion, changing object poses, contact events, and the need for temporal consistency across actions. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
