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Data encrypted in transit (TLS 1.3) and at rest (AES-256) across every storage and processing surface – source files never travel unprotected.
Data Annotation Service
Data that powersbetter AI
Master your data from source to scale.
We collect, clean, structure, and annotate data so it becomes ready for AI training, product development, and advanced analytics – turning raw information into production-ready datasets that improve accuracy, reduce manual effort, and scale with your business.
99% accuracy SLA2M+ assets deliveredGDPR / ISO 27001 aligned60+ clients worldwide
Overview
AI excellence is built on data integrity, and we take full ownership of that foundation.
Full-lifecycle data management accelerates the path from raw information to high-performing AI models. By synchronizing expert domain knowledge with rigorous validation, the process converts unstructured data into fully engineered, high-fidelity intelligence. This ensures a seamless transition into production, maximizing model accuracy while reducing the time spent on data troubleshooting.
- 2M+Assets annotated
- 99%Accuracy SLA
- 60+Active clients
- 300+Specialist annotators
Why it matters
Bad data doesn't just slow you down – it costs you the model
Poorly labeled or inconsistent datasets create bottlenecks, lower accuracy, and compound the cost of fixing errors downstream. Here's how the quality bar actually moves the numbers.
The same model. The same team.
Only the data quality changed.
Noisy dataset
DataX-quality
- Model accuracy74% → 99%
- Rework rate28% → 2%
- Time-to-production12 wks → 5 wks
- Annotator agreement71% → 98%
Illustrative figures across customer engagements. Actual improvements vary by use case and baseline quality.
Accuracy over volume
Accurate data leads to better decisions and more reliable outcomes.
Consistency is infrastructure
Clear guidelines, measured inter-annotator agreement, and calibration reviews – consistency that scales.
Process discipline, end-to-end
Every file passes through 2–3 QA layers before it reaches your team.
Annotation types
Every modality your model needs, labeled right
Pick a modality to preview how we structure the output. Every format ships with full guideline docs, inter-annotator agreement reports, and QA-approved ground truth.
Image
Bounding boxes, polygons, keypoints, segmentation.
Pixel-precise labelling across classification, detection, and segmentation tasks – the foundation for any computer-vision model.
Delivered as
COCOYOLOPascal VOCPNG Masks
Mastering
Latest labelling tool
Our process
Seven steps from raw data to AI-ready delivery
Every engagement runs through the same discipline – so there's no ambiguity about where we are, what's coming, or how quality is measured.
- 01
Requirements
Step 01Learn your goals, project scope, data types, and quality expectations.
- 02
Sourcing & Prep
Step 02Collect, clean, organize, and prepare the source data for annotation.
- 03
Guidelines
Step 03Define labeling rules, edge cases, and quality standards.
- 04
Training
Step 04Annotators trained on guidelines, workflow, and output quality.
- 05
Execution
Step 05Data annotated to the approved guidelines and project specs.
- 06
Multi-Level QA
Step 062–3 review layers catch errors and maintain accuracy.
- 07
Delivery
Step 07Validated output delivered in AI-ready formats.
Data security & trust
Your data as our data
Security, privacy, and governance are built into every engagement – from NDA-first onboarding through encrypted delivery and verified deletion.
Day 1
NDA-first workflows
Mutual NDAs in place from day one, with annotators bound to per-project confidentiality and clean-desk policies before any data touches the floor.
GDPR
GDPR & regional compliance
Workflows aligned with GDPR, Vietnam PDPL, and Australia Privacy Act – PII handled under documented retention, residency, and disclosure rules.
MFA · RBAC
Role-based access control
Least-privilege access, MFA on every account, and project-scoped permissions – annotators only see the data their assigned task requires.
Full audit
Audit trails & data lineage
Every change tracked – who touched what, when, and why. Versioned guidelines and per-batch agreement scores ship with every delivery.
Verified erase
Secure deletion & retention
Data purged on contractually-agreed timelines with cryptographic erasure on completion – nothing lingers beyond what your engagement requires.
Why choose us
Our Partner comes back to
The discipline, the flexibility, and the numbers – here's what teams rely on us for, again and again.
GDPR
aligned
Enterprise-grade workflows
Structured project management, versioned guidelines, and audit trails – ready for regulated industries.
48hr
typical kickoff
Flexible project support
From one-off labelled batches to ongoing annotation ops – scale up or down without contract drama.
6
modalities
Multi-format expertise
Image, video, text, audio, document, and 3D – one team, one point of contact, every modality covered.
99%
accuracy SLA
Strong QA process
2–3 layers of review with measured inter-annotator agreement – every dataset ships with a quality report.
2M+
assets delivered
Scalable delivery
From 10K rows to 10M – consistent throughput with elastic annotation teams and batched QA.
60+
clients worldwide
Reliable partner for AI, ML, analytics
Trusted by 60+ teams worldwide – we speak the language of both data engineers and ML researchers.
Case studies
Explore some of our successful data projects
Four engagements across annotation, analytics, and data-product work – each with a measurable outcome behind the dataset.
Ready to Get Started
Let's build what's next
Share your challenge – AI, data, or infrastructure. We'll scope your project and put the right team on it.