Every machine learning model learns from examples, and every example needs a correct label. A model that detects defects needs thousands of images with the defects marked. A support chatbot needs conversations tagged by intent. An LLM needs responses ranked by quality. If those labels are inconsistent or wrong, the model learns the wrong thing — and no amount of tuning fixes it later.
Labeling is also slow, repetitive, and easy to underestimate. Engineers end up spending weeks drawing boxes or tagging text instead of building the model, or a low-cost vendor delivers labels that need to be redone.
Our data labeling services give AI teams accurate, consistent training data without pulling engineers off their real work. We write clear labeling guidelines with you, start with a sample batch so you can check quality before committing, then scale with multi-step review built in.
What are data labeling services? Data labeling services add accurate tags, categories, boxes, or ratings to raw data — text, images, video, audio, or documents — so machine learning models can learn from it. A data labeling company provides trained annotators, written guidelines, quality review, and delivery in the format your model training pipeline expects. Data labeling and data annotation mean the same thing.
Data Labeling Services at a Glance
Question | Answer |
|---|---|
What it is | Human labeling of text, images, video, audio, and documents to create AI training and evaluation data |
Data types | Text and NLP, images, video, audio and speech, documents, LLM prompts and responses, code |
Quality process | Written guidelines, gold-standard examples, multi-step review, spot checks, and agreement checks |
Tools | Your labeling platform or a set-up tool such as Label Studio or CVAT |
Output formats | COCO, YOLO, Pascal VOC, JSON, JSONL, CSV, or your custom schema |
How to start | A sample batch on your own data, reviewed by you before any larger commitment |
When to Outsource Data Labeling
Your engineers are labeling data. Their time is better spent on models and product.
You need volume fast. A new model or feature needs thousands of labeled examples on a deadline.
Your labels are inconsistent. Different people interpret the task differently, and model accuracy suffers.
You need expert judgment. Tasks like rating LLM answers or reviewing code need skilled reviewers, not crowd workers.
You need evaluation data. You want a trusted test set to measure model quality before release.
Labeling never ends. Production models need fresh labels as data drifts.
Our Data Labeling Services
Text and NLP labeling
Text classification, sentiment, intent, named entity recognition (NER), topic tagging, relation labeling, content moderation, and translation checks for chatbots, search, and NLP models.
Image labeling
Image classification, bounding boxes, polygons, semantic and instance segmentation, and keypoints for computer vision models such as defect detection, retail shelf analysis, and document image processing.
Video labeling
Object tracking across frames, action and event tagging, and timestamped segments for video analytics and monitoring use cases.
Audio and speech labeling
Transcription, speaker identification, timestamps, intent, and sound event tagging for speech recognition and voice assistants.
Document labeling
Key-value extraction labels, table and layout labeling, OCR correction, and document classification for invoice, contract, and form processing models.
LLM and generative AI data
Prompt and response writing, response ranking and preference data for RLHF, output quality and safety ratings, and evaluation sets for LLM applications and RAG systems.
Code data labeling
Code review labels, bug and fix annotation, and coding task evaluation by experienced developers. See our dedicated AI training data service for AI labs.
Label review and cleanup
Audit and fix an existing labeled dataset — finding mislabeled, duplicate, or ambiguous examples that are hurting model accuracy.
Common Labeling Tasks Explained
Task | What it means | Typical use |
|---|---|---|
Classification | Assign one or more categories to an item | Spam detection, ticket routing, product categorization |
Bounding box | Draw a rectangle around each object | Object detection |
Polygon / segmentation | Outline the exact shape or pixels of an object | Defect detection, scene understanding, precise object outlines |
Keypoints | Mark specific points such as joints or corners | Pose estimation, gesture recognition |
Named entity recognition | Tag names, dates, amounts, and other entities in text | Information extraction, search |
Sentiment / intent | Label emotion or the goal behind a message | Customer feedback, chatbots |
Transcription | Convert speech to text with timestamps | Speech recognition, call analytics |
Preference ranking | Rank or compare model responses | RLHF and LLM fine-tuning |
Evaluation rating | Score outputs against a rubric | Model testing before release |
How We Keep Labels Accurate
Quality comes from process, not promises. Every project follows the same steps:
Clear guidelines. We write labeling instructions with you, including examples of correct, incorrect, and edge cases.
Gold-standard set. You or your experts label a small reference set that our team is checked against.
Pilot and calibration. Annotators label a sample, we compare results, and refine the guidelines before scaling.
Multi-step review. Labels are reviewed by a second person; hard or disputed items go to a senior reviewer.
Agreement checks. For subjective tasks, several people label the same items so disagreements can be measured and resolved.
Ongoing spot checks. We sample delivered batches against the gold set and share quality reports with you.
How It Works
Step 1: Share the task
Tell us what your model needs to learn, the data type, rough volume, and your deadline.
Step 2: Guidelines and sample batch
We agree labeling guidelines and label a sample batch of your data. You review it before any larger commitment.
Step 3: Scale up
Once quality is approved, we scale the team and deliver in batches on an agreed schedule.
Step 4: Review and deliver
Each batch passes review and is delivered in your required format, with a quality summary.
Step 5: Iterate
As your model improves, we adjust guidelines, focus on hard cases, or move on to evaluation data.
Data Security
NDA before data is shared
Work inside your environment or your labeling platform when required
Access limited to the people working on your project
No reuse of your data for any other purpose
Deletion of working copies at the end of the project on request
Engagement Models
Model | How it works | Best when |
|---|---|---|
Sample batch | A small labeled set on your own data to test quality and fit | You're evaluating a labeling partner |
Project-based | Priced per item, per hour, or per batch for a defined dataset | You have a clear volume and deadline |
Ongoing labeling team | A dedicated team that labels new data continuously | Production models need regular fresh labels |
Who This Is For
AI and ML startups | Product teams adding AI features | AI labs and LLM teams | Enterprises with production models |
|---|---|---|---|
Need training data fast without hiring a labeling team | Need labeled data for a first classifier, extractor, or chatbot | Need expert preference data, ratings, and evaluation sets | Need continuous labeling as data changes |
Why Work With Codersarts
We're an AI engineering company, not only a labeling vendor. Our team understands how labels are used in training, so we design guidelines that match your model's needs and flag data problems early. We can also help with what comes next — data preparation, model training, and machine learning development — so labeled data actually turns into a working model.
You start with a sample batch, see the quality yourself, and scale only when you're satisfied.
Frequently Asked Questions
What is the difference between data labeling and data annotation?
There's no practical difference. Both mean adding tags, categories, boxes, or ratings to raw data so AI models can learn from it. "Annotation" is often used for images and video, and "labeling" for classification tasks, but the terms are used interchangeably.
How much do data labeling services cost?
Pricing depends on the data type, task complexity, required expertise, and volume. Simple classification costs far less per item than detailed segmentation or expert LLM ratings. We price per item, per hour, or per batch after reviewing a sample of your data.
How do you ensure labeling quality?
Through written guidelines, a gold-standard reference set, a pilot batch, multi-step review, agreement checks on subjective tasks, and ongoing spot checks with quality reports shared with you.
Can we test you before committing to a large project?
Yes. Every engagement can start with a sample batch on your own data, so you can review quality, speed, and communication first.
What formats do you deliver labels in?
Common formats include COCO, YOLO, and Pascal VOC for computer vision, JSON and JSONL for text and LLM data, and CSV for tabular tasks. We can match any custom schema your training pipeline uses.
Can you work in our labeling tool?
Yes. We can work in your existing platform, or set up an open-source tool such as Label Studio or CVAT if you don't have one.
Do you provide RLHF and LLM evaluation data?
Yes. We write prompts and responses, rank and compare model outputs, rate responses against your rubric, and build evaluation sets for LLM and RAG applications.
How fast can you deliver labeled data?
A sample batch can usually be turned around within days of agreeing the guidelines. Larger datasets are delivered in batches on an agreed schedule based on volume and complexity.
Is our data kept confidential?
Yes. We sign an NDA before receiving data, restrict access to the project team, never reuse your data, and can work entirely inside your own environment.
Related Data Services
AI Data Services — get your data ready for RAG, LLMs, and machine learning
Data Classification — classification models built on labeled data
Web Scraping — collect raw data to label
Data Readiness Assessment — check whether your data is ready for AI
Ready to Get Accurate Training Data?
Tell us about your model, your data type, and your volume. We'll propose guidelines and label a sample batch so you can judge the quality yourself.
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