Sourcing
Data Collection
High-quality, domain-specific datasets collected and prepared for AI and machine learning applications.
- Custom sourcing
- Domain datasets
- Data preparation
AI Data & Intelligence
Powering AI With Better Data.
We provide high-quality data collection, annotation, and AI evaluation solutions that help businesses build and improve intelligent AI systems.
Pilot batches available before full-scale work begins.
Built for teams developing the next generation of AI
- Pilot batch before full scale
- Guidelines agreed before work starts
- Human review on every batch
- Delivered in your formats
What we do
Data solutions built for AI.
From raw data to AI-ready datasets, NORBEON helps organizations create reliable data pipelines for modern artificial intelligence.
Labeling
Data Annotation
Accurate human-powered annotation across images, text, audio, video, and other data formats.
- Image
- Text
- Audio
- Video
Vision
Computer Vision
Image and video annotation for object detection, classification, segmentation, OCR, and visual AI.
- Detection
- Segmentation
- OCR
Language
Text & NLP
High-quality text annotation, sentiment analysis, classification, and language data for NLP models.
- Entities
- Sentiment
- Classification
Speech
Audio & Speech
Speech transcription, audio classification, speaker labeling, and conversational AI data.
- Transcription
- Speaker labeling
- Audio events
Evaluation
AI Model Evaluation
Human evaluation and quality assessment designed to improve the accuracy and reliability of AI systems.
- Human feedback
- Rubric review
- Preference ranking
We also support multimodal data, data quality and validation, human-in-the-loop evaluation, and custom AI data solutions.
Discuss your projectWhy NORBEON
Reliable data for AI systems that need to perform.
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High-Quality Datasets
Structured and validated data built around your requirements.
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Scalable Operations
Scale projects from small datasets to large production workflows.
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Human Quality Control
Human-in-the-loop processes designed to improve annotation accuracy.
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Business-Focused Delivery
Solutions designed around your AI model and business objectives.
How we work
From raw data to AI‑ready intelligence.
Every stage has a clear output you can review before the next one begins.
- 01
Collect
Source or gather the data your model needs, to your specification.
- 02
Annotate
Trained annotators label to guidelines agreed with your team.
- 03
Validate
Reviewers audit samples and resolve disagreements in every batch.
- 04
Evaluate
Output is checked against your acceptance criteria before release.
- 05
Deliver
AI-ready datasets in the formats your training pipeline reads.
Industries
Built for the teams building what comes next.
AI & Machine Learning
Training, fine-tuning and evaluation data for models at every stage.
Computer Vision
Labeled image and video data for detection, segmentation and tracking.
Robotics
Annotated visual and sensor data for perception and manipulation.
Autonomous Systems
Frame, sensor and edge-case data for perception models.
E-commerce
Product, catalogue and search data for retail AI.
Healthcare Technology
Structured, reviewed data for teams building health-focused AI.
SaaS & Enterprise
Data preparation for AI features inside business software.
Conversational AI
Dialogue, intent and speech data for assistants and agents.
Projects
Data solutions built around real AI challenges.
Representative examples of the work we take on. Every engagement is scoped to your model, data type and quality bar.
Conversational AI Dataset
How a team building a support assistant could collect, label and review conversation data for intent, entities and response quality.
Explore use case
- Intent and entity labeling on customer conversations
- Response rating against a written quality rubric
- Edge-case review before each delivery
Visual Recognition Dataset
How image and video data could be labeled for detection and segmentation, with reviewers checking hard cases before delivery.
Explore use case
- Bounding boxes, polygons and segmentation masks
- Class taxonomy and edge-case rules agreed up front
- Sample audits on every batch
Product Intelligence Dataset
How a retail team could turn messy catalogue data into structured product attributes for search, recommendations and tagging models.
Explore use case
- Product attribute tagging and categorization
- Catalogue cleanup and deduplication
- Search relevance judgments
Model Evaluation & Human Feedback
How trained reviewers could rate, rank and compare model outputs so a team knows where its model falls short.
Explore use case
- Rubric-based response rating
- Preference ranking between model outputs
- Red-teaming and safety review
AI data capabilities
Every data type your model learns from.
Annotation and evaluation workflows for single-modality and multimodal AI systems.
Image
Classification, bounding boxes, polygons, segmentation and keypoints.
Text
Entities, intent, sentiment, classification and conversation labeling.
Audio
Transcription, speaker labeling, audio classification and sound events.
Video
Frame-by-frame labeling, object tracking and activity tagging.
3D / Sensor
Point cloud and sensor data labeling, including 3D cuboids.
Multimodal
Linked image, text and audio data for multimodal models.
Ready to build better AI?
Tell us about your AI project and we'll help you design the right data solution.
FAQ
Questions teams ask before starting.
What types of data do you work with?
Image, video, text, audio, 3D and sensor data, and multimodal datasets that combine them. Each project is set up around your data type, taxonomy and model goals.
Can we start with a small pilot?
Yes. Pilot batches are available before full-scale work begins, so you can check the output against your quality bar before committing to larger volumes.
How do you maintain annotation quality?
Guidelines and edge-case rules are agreed before the first task goes live. Reviewers audit samples in every batch, check disagreements and share error reports as the work runs.
What formats do you deliver in?
We deliver in the formats and schema your training pipeline already reads. Output format is agreed during scoping.
How is our data handled?
Access, storage and confidentiality requirements are agreed with you before any data is shared, and the work follows them throughout the project.
Contact
Let's build better AI together.
Share a few details about your model and data. We'll come back with questions and a practical starting point.
Email
LinkedIn
Location
Based in India. Working with AI teams worldwide.