Classification
Each item gets one label from a set you agree on, with a clear rule for what counts as "other".
For routing, intent detection and content filters
Annotation for AI teams
Data annotation and labeling, checked batch by batch
Your model learns whatever its labels teach it, mistakes included. So our data annotation team labels your text, images, video and audio by hand: classification, entity tags, sentiment, bounding boxes and segmentation. Every batch gets a multi-level review before it reaches you, and the questions annotators raise go back into your guideline.
My refund issue for the blue kurta product
still hasn't reached Pune place.
sentiment negative
- annotator 1 negative
- annotator 2 negative
- agreed
✓ guideline updated new rule: sarcasm
The short answer
What is data annotation?
Data annotation is the work of adding labels to raw data so a machine learning model can learn from it. A person marks what each item is, or what's in it, and the model learns to make the same calls on data it hasn't seen.
In practice that means tagging a support ticket as a refund request, drawing a box around every car in a street photo, or marking where one speaker stops and the next one starts. The labels are the answers your model studies from, so their mistakes become its mistakes.
Human-in-the-loop means people make or check the labels instead of leaving the whole job to software. That matters most when the label is a judgment call, like sarcasm, or a car half hidden behind a bus. For worked samples, see text data and image data. For what separates good annotation from bad, see the annotation quality guide.
Data annotation at IndecalAI
- Data types
- Text, images, video and audio.
- Label types
- Classification, entity tagging, sentiment, bounding boxes, segmentation and audio labels.
- Who labels
- Trained annotators from a network of 30,000+ people worldwide.
- Quality
- A multi-level review on every batch before delivery.
- Languages
- Languages worldwide, with deep coverage of Indian languages well past Hindi and English.
- Tools
- Our own Image Annotation tool for image work.
- Your data
- NDA first, used only for the agreed scope, returned or deleted when you ask.
- Contact
- One named contact who replies within one business day.
- First step
- A small free sample before you commit.
- Company
- Based in Ahmedabad, India, since 2021, with 150+ projects delivered.
What we label
Six kinds of labels, done by people and checked twice
Text gets classified, tagged for entities and labeled for sentiment. Images and video get bounding boxes and segmentation, and for images that happens in our own Image Annotation tool. Audio can be labeled too, for speech and other sound data.
Entity tagging
Names, places, products, dates and amounts marked in the text, so your model can pull them out on its own.
For assistants, search and document reading
Sentiment
Reviews, chats and calls labeled positive, negative or neutral, by people who catch sarcasm and local slang.
For support analytics, review mining and brand tracking
Bounding boxes
A tight box around every object your model has to find, each with its class, drawn by people who follow your rules for partly hidden things.
For object detection, driver assistance and shelf checks
Our tool: Image Annotation Free sample of bounding boxes
Segmentation
Every pixel assigned to a class, for when a box is too rough and your model needs the exact outline.
For scene understanding, medical images and robotics
Our tool: Image Annotation Free sample of segmentation
Audio labeling
Stretches of audio marked as speech, music, noise or silence, or tagged for the speaker, the emotion or the sound.
For speech recognition, voice bots and sound detection
The samples on these cards are illustrations, not client data. If the work is mostly visual, computer vision goes into more detail. If your audio still needs turning into text first, start with transcription.
Where it's used
What labeled data teaches a model
Common jobs for labeled data, and the kind of label behind each one. They're examples of the work, not a client list.
Routing support tickets
Tickets labeled by topic and urgency teach a model to send each one to the right queue, and to spot the ones that can't wait.
Knowing what a message is about
Names, products, places and dates tagged in text help a search tool or an assistant pull out what the customer actually asked for.
Reading customer feedback
Reviews and survey answers labeled for sentiment, sarcasm included, so "great, another delay" doesn't get counted as praise.
Driver assistance and dashcam video
Cars, people, signs and lanes marked frame by frame, so an object detection model learns what's on the road.
Products on store shelves
Products outlined on shelf photos, so a model can count what's there and notice what's missing.
Voice assistants and call audio
Speech, silence, noise and speaker turns marked in recordings, so a speech model learns which parts to listen to.
Why labels disagree
Good labels start with good instructions
Label quality usually depends more on the instructions than on the tool. Two annotators can read the same vague guideline, both be perfectly reasonable, and still disagree. That disagreement ends up in your training data.
The fix isn't glamorous. Write down the label set. Add examples for the awkward cases, the ones that sit between two classes. Update the guideline whenever annotators run into something it didn't cover. Then measure how often two annotators give the same label to the same item, and go and look at where they split.
Choosing a labeling partner? Our guide on how to choose a data annotation vendor lists the questions worth asking.
Before labeling starts
Settle these first
Four decisions made up front stop the same argument from happening a thousand times inside your data.
The label set
How many classes there are, what they're called, and what counts as "other". Agree it before the first item gets a label.
The awkward cases
Unclear, partial or overlapping items. Add examples for the ones that sit between two classes, and write the decision into the guideline.
Who breaks the tie
Reviewers will disagree now and then. Decide who settles it, so the same call gets made every time.
The output you need
Tell us what your training pipeline expects to receive, so the labels fit it without another round of cleanup.
How it works
From your examples to reviewed labels
One person on our side owns your project and answers the questions. Here's how a labeling job usually runs.
Task and examples
You share the task and some examples of the data.
NDA first
The NDA is signed before any of your data moves.
Guideline
We settle the classes, the edge cases and how disagreements get resolved.
Free sample
We label a small sample free, so you can check it before you commit.
Labeling
Trained annotators work through the batch, and their questions go back into the guideline.
Review and delivery
Multi-level review checks everything before it reaches you.
Languages
Text labeled by people who read the language
Slang, spelling habits and mixed languages included. We work worldwide and go deep on Indian languages, well past Hindi and English. Our page on Indian language data has the details.
Glossary
Annotation jargon, explained
The words that come up when you plan a labeling project, and what each one means.
- Annotation guideline
- The written rules annotators follow: the label set, examples for each label, and decisions on the awkward cases. Most label quality problems start here.
- Label set
- Every class an item can get, including what counts as "other". Some teams call it the taxonomy or the ontology.
- Inter-annotator agreement
- How often two annotators give the same item the same label. Low agreement usually points to an unclear guideline, not careless people.
- Edge case
- An item that sits between two labels, or fits none of them. Each one gets a written decision so it's labeled the same way every time.
- Bounding box
- A rectangle drawn around an object in an image or video frame, with the object's class attached.
- Segmentation
- Marking the exact outline or pixels of an object or region, instead of a rectangle around it. It takes longer and gives a model more detail to learn from.
- Entity tagging
- Marking spans of text as people, places, products, dates or other types. You'll also see it called named entity recognition labeling, or NER.
- Sentiment labeling
- Marking whether a piece of text reads as positive, negative or neutral. Some projects add finer grades, or a separate label for sarcasm.
Related work
What comes before and after labeling
One team can collect the data, label it and check your model against it. You can also compare all eight services. Example category: English e-commerce support text, labeled for sentiment.
Computer vision
Detection, segmentation and recognition for images and video, in more depth.
Transcription
Audio turned into reviewed text first, ready to be labeled.
Data and model evaluation
Independent accuracy numbers against targets you set.
Data collection
New data gathered for your domain and languages, then labeled here.
Translation and localization
Labeled text carried into the other languages your model has to handle.
FAQ
Questions about data annotation
Short answers about labeling with us. The services FAQ covers the other services and how they fit together.
What is the difference between data annotation and data labeling?
Mostly nothing. Both mean adding labels to data so a model can learn from it. Some teams keep "labeling" for simple tags, like one class per item, and use "annotation" for richer marks such as boxes, outlines and entity spans.
What annotation types do you support?
Text classification, entity tagging and sentiment labeling, bounding boxes and segmentation for images and video, and labeling for audio.
How do you check quality?
Every batch goes through multi-level review before delivery. If you want independent numbers, our data and model evaluation service can measure accuracy against targets you set.
Who does the labeling?
Trained annotators, and for text, people who read the language, slang and spelling habits included. Their questions go back into the guideline, so the rules get clearer as the work goes on.
Can you label text in Indian languages?
Yes. Text can be labeled in Indian languages by people who read them, and that goes well past Hindi and English. Tell us the language and the dialect.
What do you need from us to start?
The task, a few examples of your data, the labels you want and a rough volume. If you already have a guideline, send it too. If you don't, we settle the classes, the edge cases and how disagreements get resolved with you before labeling starts.
How long does a data annotation project take?
It depends on the volume, the label types and how settled the guideline is, so there's no standard turnaround we can promise. The free sample is the quickest way to see the work, and the timeline gets agreed along with the full scope.
Can we try a small batch first?
Yes. Send a short brief with the task, a few examples and a rough volume, and we'll label a small sample free before you commit.
Does our data stay private?
Yes. The NDA is signed before any data moves, we use it only for the scope we agreed, and only the people working on your project can get to it. It's returned or deleted when you ask.
Start with a free sample
Pick the kinds of data, say what labels you need and roughly how much, and send it. We'll label a small sample free before you commit.
- Your named contact replies within one business day.
- We sign an NDA before any data changes hands.
- We label your free sample, then agree the full scope.
This opens your email app with the brief filled in. Nothing is sent until you press send. You can also use the contact page.