Ingest & scope
Data is received securely, sampled and profiled. We agree the taxonomy, edge cases and accuracy targets.
Data Labeling & Annotation
Data labeling that gives AI its ground truth
Aarvexa delivers accurate, consistent training data for images, video, text, audio and 3D, labeled by trained teams, pre-labeled by AI and verified through multi-layer quality assurance.
- Every data type
- Multi-layer QA
- Secure by default
street-scenes-v3Task 4,218 / 5,000
- Layers
- car1
- person0
- traffic light0
- drivable area0
- –QA score
1Annotate2Review3ApprovedAI pre-label 82% accepted
Annotators
Trained · NDA-bound
Gold-standard check
Running
What we label
Eight services around one quality core
Pick a service on the ring. Every one runs through the same annotation, review and QA engine.
Image annotation
Bounding boxes, polygons, keypoints and pixel-perfect segmentation for vision models.
- Boxes & polygons
- Semantic & instance masks
- Keypoints & landmarks
Data types
Every kind of data, labeled the way your model needs
Image annotation
Pixel-accurate ground truth for vision models
From fast bounding boxes to detailed segmentation masks, with class taxonomies and attribute tags built around your model.
- Used for
- Detection, segmentation, inspection, retail recognition
Bounding boxPolygonKeypointsSegmentation
Our workflow
From raw data to model-ready labels in six stations
Work moves down the line. Nothing ships until it has been annotated, reviewed and audited.
AI pre-label
Models propose first-pass labels so people verify and correct instead of starting from zero.
Human annotate
Trained annotators label to your written guidelines, with domain specialists for complex data.
Peer review
A second pair of eyes checks every task type, and disagreements go to a reviewer.
QA audit
Gold-standard checks and statistical sampling measure accuracy against your threshold.
Deliver & learn
Data ships in your format. Model feedback and error analysis sharpen the next batch.
Quality assurance
Quality is measured at four levels, not promised
Typical targets shown. Final thresholds are agreed per project after the pilot.
98%target accuracy
Gold-standard checks
Hidden pre-labeled tasks measure each annotator continuously, so drift is caught within hours rather than at delivery.
94%inter-annotator agreement
Consensus voting
Ambiguous items are labeled by several people. Agreement is measured and disagreements are resolved by a senior reviewer.
100%tasks reviewed
Multi-layer review
Annotate, review and audit are separate roles. The person who labeled a task never approves it.
90%issues fixed in one cycle
Feedback loop
Error patterns feed guideline updates and annotator coaching, and your model's failures guide what we relabel.
Industries
Built for the teams shipping real-world AI
Scroll through the stack. Each industry has its own taxonomy, edge cases and review rules.
01 / 06
Autonomous driving
Every lane, light and pedestrian, labeled
Camera, LiDAR and radar labeling for perception teams, with sequence-level consistency and rare-scenario mining.
Typical labels
- 3D cuboids
- Lane lines
- Traffic signs
- Free space
02 / 06
Healthcare & medical imaging
Clinician-guided annotation
Segmentation and classification of scans and records by trained reviewers, under strict privacy controls.
Typical labels
- Organ segmentation
- Findings
- Report entities
- De-identification
03 / 06
Retail & e-commerce
Catalogues machines can understand
Product attributes, shelf recognition, visual search and review sentiment labeled at catalogue scale.
Typical labels
- Attributes
- SKU boxes
- Taxonomy
- Sentiment
04 / 06
Agriculture & geospatial
Crops, fields and land from above
Satellite and drone imagery labeled for crop health, yield, land use and change detection.
Typical labels
- Crop types
- Field boundaries
- Disease spots
- Land use
05 / 06
Finance & insurance
Documents and claims, structured
Forms, invoices, statements and claim photos labeled for extraction, fraud and risk models.
Typical labels
- Key-value
- Tables
- Damage areas
- Entities
06 / 06
Manufacturing & robotics
Defects and grasps, defined precisely
Defect classes, part positions and grasp points labeled to teach inspection and robotics models.
Typical labels
- Defects
- Part pose
- Grasp points
- Assembly steps
Why outsource to Aarvexa
In-house, crowd or a managed team?
A fair look at the trade-offs, based on what AI teams usually tell us.
In-house teamCrowd marketplaceAarvexaRecommended
Domain expertise
Strong, but pulled from core work
Anonymous and uneven
Trained and specialised per project
Consistency
Varies with workload
Low, many contributors
High, stable core team
Data security
Controlled
Hard to control
Secure environments and NDAs
Speed to scale
Slow to hire
Fast to start
Fast ramp with a managed team
Quality assurance
Ad hoc
Basic checks
Multi-layer QA and audits
Cost control
Fixed headcount
Hidden rework
Predictable per-unit pricing
Delivery formats
Labels delivered in the format your pipeline already reads
Pick a format to see a sample of what lands in your storage or API.
annotations.jsonjson
{
"images": [{ "id": 1, "file_name": "street_0042.jpg", "width": 1280, "height": 720 }],
"annotations": [
{ "id": 7, "image_id": 1, "category_id": 3,
"bbox": [412, 288, 164, 120], "area": 19680, "iscrowd": 0 }
],
"categories": [{ "id": 3, "name": "car" }]
}- Works in
- CVAT
- Label Studio
- Labelbox
- SuperAnnotate
- Prodigy
- V7
- Scale-compatible APIs
- Custom tooling
How to start
Start with a free pilot, scale when the numbers convince you
Pilot batch
Send a sample. We label it to your spec and share the accuracy report, so you judge quality before committing.
- Free sample on request
- Guideline draft
- Accuracy report
Calibrate
Guidelines, edge cases and QA thresholds are tuned with your team until the labels match what your model needs.
- Edge-case library
- Agreed thresholds
- Tool setup
Ramp the team
A trained, dedicated team scales up, with team leads and reviewers tracking quality daily.
- Dedicated annotators
- Daily quality reports
- Weekly sync
Scale & optimise
Volume grows while per-unit cost and error rates fall, driven by pre-labeling and feedback from your model.
- Model-assisted labeling
- Throughput planning
- Continuous audits
Security & trust
Your data is handled like it is ours
Encrypted transfer
Data moves over encrypted channels and rests encrypted
Vetted, NDA-bound team
Background-checked annotators under confidentiality agreements
Role-based access
People see only the projects and data they need
Audit logs
Every access and label change is recorded
Privacy-aligned
Processes designed to align with GDPR and your policies
Secure environments
Work happens in controlled workspaces with no local copies
FAQ
Frequently Asked Questions
Straight answers on data labeling: applications, technology, safety, cost and timelines.
Data labeling, or annotation, is the process of adding meaningful tags to raw data such as images, video, text, audio and documents, so machine learning models can learn from examples and be evaluated against ground truth.
Model quality depends on training data quality. Accurate, consistent labels improve accuracy, reduce bias and cut the time spent debugging models that learned from noisy data.
Managed data labeling and annotation across images, video, text, audio, 3D and documents, with pre-labeling, multi-layer quality assurance, secure delivery and ongoing feedback from your model results.
Machine learning and AI teams, product companies adding AI features, start-ups preparing training data and enterprises that need consistent, secure labeling at scale.
Book a free consultation and share a sample. We label a pilot batch to your guidelines and report the accuracy before you commit to a larger volume.
Send a sample. Get it labeled free.
Share a few hundred items and your guidelines. We return a labeled pilot batch with an accuracy report, so you decide with evidence.