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DATA ANNOTATION

Data annotation services

Complex data.Reliable training annotations.

Define classes, boundaries and attributes around your model task. Align the rules in a small pilot, then annotate and review each batch with supporting quality records.

Discuss annotation needsView annotation samples
Urban road · 2D objects & lane lines
Urban road · 2D objects & lane lines
Task definitionClasses · attributes · boundaries
Quality basisRules · review · traceability
DeliverablesLabels · reports · versions

ANNOTATION VIEW

BUILT FOR TRAININGConsistent rules connect annotation, review and delivery, from class definitions to batch acceptance.

SERVICE SCOPE

Different data types. One training objective.

From visual perception to language understanding, define task rules first, then shape a suitable annotation plan. The scope below can be adapted to the project.

01

Image

IMAGE

Define classes and boundaries, from object boxes to pixel-level regions.

  • Detection
  • Segmentation
  • Keypoints
02

Video

VIDEO

Connect consecutive frames and behavior events with stable object IDs.

  • Tracking
  • Events
  • Temporal attributes
03

3D / Point cloud

POINT CLOUD

Describe spatial objects through position, dimensions and orientation.

  • 3D cuboids
  • Point classes
  • Orientation
04

Multimodal

MULTIMODAL

Connect images, point clouds and context through shared object semantics.

  • Sensor links
  • Object matching
  • Consistency
05

Text annotation

TEXT

Structure language tasks with entities, intent, sentiment and relations.

  • Entities
  • Classification
  • Relations
06

Audio annotation

AUDIO

Connect sound with usable labels through transcription, speakers and timing.

  • Transcription
  • Speakers
  • Audio events

ANNOTATION SAMPLES

Explore the task. Understand the output.

Switch between data types to explore typical tasks, input requirements and possible delivery formats. Align field definitions and quality requirements.

IMAGEANNOTATION VIEW
Vehicles, pedestrians and lane annotations in an urban road scene.
Vehicles, pedestrians and lane annotations in an urban road scene.
  • Vehicle
  • Pedestrian
  • Cyclist
  • Lane

IMAGE

A clear definition for every object

Typical tasks
Object detection, semantic segmentation, lane lines, keypoints and attributes.
Inputs to prepare
Images, class definitions, attribute rules and boundary examples.
Delivery formats
COCO, YOLO or an agreed JSON schema can be evaluated.
Discuss this annotation type

DELIVERABLES

Annotations with the records needed to review them.

Agree on schemas and acceptance criteria during the pilot so each batch can be understood, checked and reused.

Structured annotations

Classes, geometry, attributes and object IDs in the agreed structure.

Rules & class definitions

Class dictionaries, attribute rules, boundary examples and change history.

Quality & issue records

Review records, issue categories, rework status and batch acceptance notes.

Batch & version manifest

File indexes, count reconciliation, delivery versions and key relationships.

PROCESS & QUALITY

Align the standard. Then scale the delivery.

Each stage has a defined output. Sampling ratios, schedules and acceptance thresholds are agreed after project evaluation.

  1. 01

    Scope & rules

    Define the model task, class boundaries, inputs and acceptance approach.

    Output: task brief & draft rules
  2. 02

    Pilot & calibration

    Test the rules on a small batch and align difficult or disputed cases.

    Output: agreed samples & rule version
  3. 03

    Production & review

    Annotate in batches, review in stages, record issues and complete rework.

    Output: labels & review records
  4. 04

    Acceptance & delivery

    Check structure, coverage and agreed quality items before packaging.

    Output: dataset & acceptance record

CUSTOMER CASE

One image batch. Clear rules and review steps.

ANN-002 · Road vision

A road-vision team piloted 2D bounding boxes on 360 images, focusing on small objects, occlusion and truncation. Rule revisions and rechecks established consistent boundaries and issue handling.

Need
Define class and box rules for vehicles, pedestrians, cyclists and traffic signs.
Approach
Align boundaries in a small pilot, record issues and rechecks, and keep unresolved items visible.
Deliverables
Pilot annotations, rule versions, an issue log and review records.
View annotation caseExplore delivery & quality
Urban road · 2D objects & lane linesROAD VISION / ANNOTATION
360Images
4Object classes
1Pilot batch

FAQ

Before the first conversation

01What should I prepare for an initial discussion?

Prepare the data type, model objective, estimated volume, draft classes, output format and desired schedule. A small de-identified sample can help clarify boundary cases and a pilot plan.

02Can we start with a small pilot?

A pilot can be planned to validate rules, formats and quality checks. Sample size, timing and pricing depend on the complexity of the data and are set out in the project plan.

03How are quality standards defined?

Turn broad requirements into specific checks, such as class consistency, geometric boundaries, omissions and temporal relationships. Then agree on sampling, acceptance thresholds and dispute resolution.

04Can you work with our format or platform?

Adaptation can be assessed from your schema, platform requirements and de-identified samples. COCO, YOLO and KITTI are possible discussion directions; actual compatibility must be validated in a pilot.

05How are timing, cost and data handling decided?

Evaluate task complexity, volume, review requirements and delivery schedule. Access scope, retention and handover requirements should be agreed before work begins. Pricing and delivery dates follow the agreed project plan.

START WITH YOUR DATA

Define the first batch, together.

Share your data type, model objective and estimated volume to frame an initial pilot.

Discuss annotation needsBack to homepageStart with the data type, estimated volume and target format.