Data Annotation & AI Training Data Services
Data annotation is the labeling that turns raw data into training data your AI and ML models can learn from, bounding boxes and segmentation for images and video, named-entity and intent labels for text, transcription for audio, and preference data for LLMs. We provide accurate, human-in-the-loop annotation as a managed, scalable service, the unglamorous foundation that decides how good your model actually becomes.
Why choose EPIXS for data annotation
Accurate data annotation and labeling for AI/ML, image, video, text and audio, bounding boxes, segmentation, NER, transcription and RLHF, with human-in-the-loop QA. Free quote.
- Accurate labeled data that lifts model performance
- Image, video, text and audio annotation in one place
- Bounding boxes, segmentation, NER, transcription and RLHF
- Human-in-the-loop QA for consistent, reliable labels
- Scale a dedicated annotation team up or down on demand
- Secure handling of your sensitive training data
Your model is only as good as its training data
Every AI model learns from examples, and the quality of those labeled examples sets the ceiling on how well it can ever perform. That's why data annotation, the careful, consistent labeling of training data, is one of the highest-leverage and most outsourced parts of building AI. We label across every modality: drawing bounding boxes and segmenting objects in images and video, tagging entities, sentiment and intent in text, transcribing and labeling audio, and increasingly producing preference and ranking data to fine-tune and evaluate large language models. It's painstaking work where consistency is everything, which is exactly why a trained, managed team beats ad-hoc effort.
Demand here is exploding as companies move AI from pilots to production, and the buying pattern has shifted from one-off batches to continuous, dedicated labeling teams. India is a primary global hub for this work, and we provide it with the quality controls that make the labels trustworthy.
- Image & video: bounding boxes, segmentation, keypoints
- Text: named-entity, intent, sentiment, classification
- Audio & LLM: transcription, RLHF preference and evaluation data
Quality is the whole product
In annotation, inconsistent labels quietly poison a model, so our process is built around quality. We define clear labeling guidelines with you, train annotators on your specific task, and run human-in-the-loop QA with review layers and agreement checks so labels stay consistent at scale. We use AI-assisted tooling to pre-label and speed the routine cases, then have skilled people verify and handle the hard ones, faster throughput without sacrificing accuracy. And because training data is often sensitive, we handle it securely with strict access controls. The result is training data you can actually trust to improve your model, delivered as a managed service that scales with your project rather than a fragile freelance arrangement.
This service sits right inside the AI value chain we already serve, so it pairs naturally with our custom AI and RAG work, we can label the data and help build the model.
Data Annotation — FAQs
What types of data can you annotate?
Images and video (bounding boxes, segmentation, keypoints), text (named-entity, intent, sentiment, classification), audio (transcription and labeling), and LLM data such as RLHF preference and evaluation sets, across most AI/ML use cases.
How do you ensure label quality?
We define clear guidelines, train annotators on your task, and run human-in-the-loop QA with review layers and agreement checks. AI-assisted pre-labeling speeds routine cases while people verify and handle the hard ones.
Is my training data kept secure?
Yes. We handle training data with strict access controls and secure processes, which matters because annotation data is often sensitive or proprietary. Security is built into how we run the service.
Can you scale for a large or ongoing project?
Yes. We staff dedicated annotation teams that scale up or down with your pipeline, suited to continuous labeling as your models evolve, not just one-off batches.
How much does data annotation cost?
Typically a per-unit or dedicated-team model based on volume, complexity and modality. We quote per project after understanding your data and guidelines. Get a free quote.
What is data annotation and do I need it for my AI project?
Data annotation is labeling examples, like marking objects in images or tagging text, so an AI model can learn from them. If you're training or fine-tuning a model, the quality of this labeled data largely decides how well it performs.
Which annotation types and QA workflow do you support, including RLHF?
We support bounding boxes, segmentation, keypoints, NER, classification, transcription and RLHF preference and evaluation data, with labeling guidelines, AI-assisted pre-labeling, multi-pass human-in-the-loop QA and inter-annotator agreement checks, on dedicated scalable teams.
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Ready to get started with data annotation?
Tell us your goals and get a free, no-obligation proposal — usually within one business day.