Data annotation and labelling services
Raise the bar for your AI’s performance with scientifically robust, precisely annotated datasets.
Why choose DevicoAI
for data annotation and labelling services
High retention rate
96%
Our dedicated team ensures consistent support and expertise, significantly above the industry average of 80%.
Wide expert network
3000
Access to over 3000 engineers and AI experts.
Proven track record
500,000
Over 500,000 man-days successfully delivered.
Support
24/7
Highly experienced management team available around the clock.
Problems data annotation and
labelling can solve
01
Unclear objectives
AI projects often begin without a well-defined hypothesis or understanding of the dataset’s intended use, leading to misaligned training results.
Solution: DevicoAI collaborates with your team to define clear objectives and establish a scientific framework for annotation that is tailored to your AI model’s learning requirements.
02
Fragmented and disorganised data
Data is often stored across disparate systems, lacking structure and context, which slows down annotation efforts and diminishes quality.
Solution: We consolidate and structure raw data into coherent frameworks that accelerate annotation workflows and improve downstream utility.
03
Inconsistent labelling standards
Inconsistencies in labelling result from unclear guidelines or inadequate training of annotators, leading to model confusion and reduced accuracy.
Solution: DevicoAI employs detailed annotation guidelines, cross-team standardisation, and consensus-building approaches to maintain consistency across datasets.
04
Error-prone annotation
Human annotators, without sufficient oversight, introduce errors and biases that impact the reliability of training data.
Solution: Our workflow incorporates advanced tooling, redundant labelling, and statistical validation to eliminate errors and identify biases before data reaches production.
05
Integration challenges
Annotated datasets often fail to align with the technical and architectural requirements of AI models, leading to delays and rework.
Solution: We produce datasets optimised for direct integration with your systems, including tailored file formats, metadata alignment, and schema validation.
Focus on innovation, not clerkship
Achieve quality with DevicoAI suite of data annotation and labelling services for smooth operational journey
Why you need industry-specific data annotation and labelling
Healthcare
Accenture predicts that AI applications could save the U.S. healthcare industry up to $150 billion annually by 2026.
For example, IBM Watson Health’s AI Orchestrator integrates AI applications to enhance medical imaging analysis, aiding in more accurate diagnostics.
Use cases:
- Disease detection and diagnostics
- Personalized treatment plans
- Predictive analytics for patient outcomes
Accenture predicts that AI applications could save the U.S. healthcare industry up to $150 billion annually by 2026.
For example, IBM Watson Health’s AI Orchestrator integrates AI applications to enhance medical imaging analysis, aiding in more accurate diagnostics.
Use cases:
- Disease detection and diagnostics
- Personalized treatment plans
- Predictive analytics for patient outcomes
Mastercard’s AI-driven fraud detection systems have doubled the speed of identifying potentially compromised cards, enhancing security and reducing financial losses.
Use cases:
- Fraud detection and prevention
- Real-time risk assessment
- Customer segmentation for personalized services
Amazon utilizes AI to personalize product recommendations and optimize inventory management, improving customer satisfaction and operational efficiency.
Use cases:
- Personalized product recommendations
- Demand forecasting for inventory management
- Targeted marketing strategies
General Electric’s Predix platform employs AI for predictive maintenance, helping to anticipate equipment failures and enhance operational efficiency.
Use cases:
- Predictive maintenance of machinery
- Production process optimization
- Supply chain management
DevicoAI process for data annotation
and labelling
Objective setting
We start by defining clear hypotheses and labelling objectives that align with the AI model’s purpose. This includes scoping the dataset, identifying target use cases, and establishing metrics for success.
Dataset structuring
Our team organises raw data into logical, easily navigable structures. By implementing pre-annotation steps, such as data cleaning and augmentation, we ensure the dataset is primed for accurate labelling.
Annotation and labelling
We utilise industry-leading annotation platforms and domain-specific tools to apply precise labels. This process often incorporates active learning loops, where the AI suggests labels to improve efficiency.
Quality review and validation
We apply a multi-stage review process that includes consensus labelling, statistical validation, and edge-case analysis. Special attention is given to bias identification and correction.
Custom taxonomies and metadata
Taxonomies are developed in collaboration with domain experts to ensure that labels reflect the subtleties of the data. Metadata is enriched to maximise interpretability and future usability.
Model integration
Our outputs are fully formatted for seamless integration with your AI models, including schema validation and preprocessing to reduce operational overhead.
Automation and augmentation
We leverage semi-automated tools for repetitive labelling tasks, reducing manual effort while maintaining human oversight for high-complexity annotations.
Feedback loop and iteration
Post-delivery, we enable continuous feedback from models to refine the dataset. This iterative process improves labelling quality over time, optimising your AI’s learning curve.
Secure datasets for your AI development
Turn unorganised datasets into high-quality resources that power dependable AI models
Benefits of
data annotation and labelling
Technologies we use
Microsoft Azure
- check_circle
Azure machine learning: Comprehensive service for building and deploying machine learning models.
- check_circle
Cognitive Services: Suite of pre-built AI services including vision, speech, language, and decision-making.
- check_circle
Azure Bot Service: Platform for building and deploying chatbots.
- check_circle
Azure OpenAI Service: Access to OpenAI's powerful language models.
Get in touch
Drop us a line about your project and we will contact you within a business day
Our locations
New York
521 Fifth Ave, NY 10175
+1 805 491 9331London
9 Brighton Terrace, SW9 8DJ
+44 1922 214429Warsaw
Towarowa 28, 00-847
info@devico.ioLviv
Uhorska str. 14, 79034
info@devico.ioQuestions & answers
Can I get a free data annotation and labelling sample or trial?
Yes, we offer a free sample to showcase the quality and scope of our services.
Why should I choose DevicoAI for data annotation and labelling over other companies?
We offer a comprehensive, technically rigorous approach that delivers structured, actionable data tailored to your business needs.
What factors influence the cost of your data annotation and labelling services?
Costs depend on data complexity, volume, and the specific services required.
Do you offer bulk discounts for large datasets?
Yes, we provide discounts for large-scale projects. Contact us for more details.