Images and video, labelled to the pixel
Tags, boxes, masks, keypoints and tracked objects for detection, inspection and recognition models.
Discuss it with us[00] Data Annotation & Labelling
Data Annotation and Labelling Services for Training AI Models
We label images, video, text and audio for teams training AI models: boxes and masks, entities and intents, transcripts and ratings of LLM answers.
built for
- AI and ML teams training custom models
- Computer vision product companies
- Teams building chatbots and LLM applications
- Speech and voice product teams
- Researchers with large unlabelled data sets
Illustration: a labelling queue. Each item is pre-labelled by a model, corrected by an annotator, checked by a second reviewer and scored against a gold set. On the first item, a street photo, the model found 3 objects; the annotator added 1 person · truck → van; the reviewer second pass agrees · 4 / 4; batch gold set · 98.6%. The other tasks label a support ticket and rate two chatbot answers. All values are demo values.
label.batch
key facts
- a sample batch you review before full volume
- Pilot first
- known answers mixed into every batch to catch drift
- Gold set
- COCO, YOLO, Pascal VOC, JSON or CSV exports
- Your format
- signed by every annotator on your project
- NDA
What we deliver
What you get with Data Annotation & Labelling. Nine parts of the service, each scoped with you.
A model is only as good as its labels, so quality control is built into every batch. Each project starts with a pilot set you review, then moves to full volume with second-pass checks, agreement scoring between annotators and a gold set of known answers that catches drift early.
We work in tools such as CVAT and Label Studio or inside your own labelling platform, and deliver in the format your training pipeline expects. Annotators sign confidentiality agreements, access is limited to your project, and sensitive data can be redacted before labelling starts.
- t.01
Image classification
Images sorted into your categories or tagged with attributes such as colour, condition or product type, for recognition, search and recommendation models.
- t.02
Bounding boxes
Tight boxes around every object of interest, with class and attribute labels, for object detection models such as YOLO and Faster R-CNN.
- t.03
Segmentation and keypoints
Pixel-level semantic and instance segmentation, polygons, and landmark or keypoint labels for road scenes, faces, poses and medical images.
- t.04
Video annotation
Objects tracked across frames with consistent IDs, events marked with timestamps, and clips categorised by scene or activity.
Text and audio a model can learn from
Entities, intents, sentiment and timed transcripts, labelled to a written schema with the hard cases decided up front.
Discuss it with us- t.05
Text classification and NER
Reviews, posts and documents classified by topic, and people, organisations, places, dates and amounts tagged as named entities.
- t.06
Intent and sentiment
Messages labelled by what the user wants to do and by positive, negative or neutral sentiment and its strength, for chatbots and opinion mining.
- t.07
Transcription and speaker labels
Audio and speech transcribed to text, speakers identified, recordings classified by language, and subtitles and captions timed to video.
Ratings for LLMs, and checks on every batch
Answers rated and ranked against your rubric, and a quality report with agreement and gold-set scores alongside every delivery.
Discuss it with us- t.08
LLM response rating
Model answers rated, ranked and corrected against your rubric, producing preference and instruction data for fine-tuning and evaluation.
- t.09
Quality control
Multi-step review, inter-annotator agreement checks and gold-standard samples on every batch, with a quality report delivered alongside the labels.
which approach fits
Label only what the model needs.
The cheapest label is the one you do not need. We match the label type to what the model has to do, and use model pre-labelling where it saves time without lowering quality.
Say what is in an image
label type: One or more tags per image
we usually start with: Image classification, with a model pre-labelling the easy cases
Find and count objects
label type: Boxes with classes
we usually start with: Boxes pre-labelled by a detector and corrected by people
Computer visionMeasure shapes, areas or damage
label type: Polygons or pixel masks
we usually start with: Segmentation on a small, closely reviewed set first, as it costs the most per image
Pull facts out of text
label type: Entity spans and relations
we usually start with: Entity labels to a written schema, with two annotators on the hard cases
Understand speech
label type: Timed transcripts with speakers
we usually start with: Machine transcription corrected by people, rather than typing from scratch
Answer the way your team would
label type: Ratings, rankings and corrected answers
we usually start with: Pairwise preference against a rubric written with your experts
Model training & fine-tuningDo a job an existing model may already do
label type: A few hundred labelled examples to test with
we usually start with: A small test set only. If an existing model passes it, you may not need thousands of labels.
LLM development
what a labelled frame looks like
Every box, field and outline. Drawn to your guidelines and checked before it trains a model.
Annotators mark the objects, text fields and shapes your model has to learn, and a review pass checks every batch against your guidelines before delivery.
- see.01 · box
Bounding boxes
Tight boxes around every object of interest, with class and attribute labels.
- see.02 · field
Text and document fields
Regions and fields labelled so OCR and extraction models learn what to read.
- see.03 · outline
Polygons and masks
Exact outlines for segmentation, including overlapping and partly hidden objects.
- see.04 · review
Quality control
Multi-step review and gold-standard samples on every batch you receive.
Why it matters
What changes once it is live. Each outcome is measured, not promised.
Data Annotation & Labelling is planned around these outcomes, measured against a baseline we agree with you up front.
Discuss your project- 01
Labels a model can learn from
Consistent labels across annotators and batches mean the model learns the pattern, not the noise between people.
- 02
Edge cases decided up front
Written guidelines with examples settle the awkward cases before labelling, and are updated as new ones appear.
- 03
Volume that matches your schedule
A dedicated team scales up for a large training set and down for ongoing top-ups, without you hiring or training annotators.
- 04
Your data stays confidential
Access controls, encryption, background-checked staff and confidentiality agreements protect the data you share.
How we work
How we deliver Data Annotation & Labelling
A clear five-step process, so you always know what happens next and what you will get.
Every project starts with a free, no-obligation consultation.
run.logappsinai / project
$ appsinai run --pilot-first
Step 1: Scope the task
We review sample data, agree the label set, the volume, the output format and how quality will be measured.
you get label set · volume and format · indicative price per item
we need sample data · the labels you need and what the model is for
Step 2: Write the guidelines
Clear instructions with examples and edge cases are drafted with you, so every annotator labels the same way.
you get guidelines with examples · edge-case decisions
we need examples of the tricky cases · a reviewer on your side
Step 3: Run a pilot batch
A small batch is labelled and reviewed by your team. We adjust the guidelines until the labels match what you expect.
you get labelled pilot set · quality report · fixed price for full volume · go / no-go
we need your review of the pilot labels
- gatego / no-go on the results before “Annotate at volume”
Step 4: Annotate at volume
Trained annotators label the full data set, with second-pass review, agreement checks and gold samples on every batch.
you get labelled batches · agreement and gold-set scores · weekly progress
we need data delivered in agreed batches
Step 5: Deliver and iterate
Labels are delivered in your format with a quality report, and we re-label or extend the set as your model reveals gaps.
you get labels in your format · quality report · re-labels and top-ups
we need feedback from model training
↺ results feed the next improvement cycle$
Security & control
Training data handled like production data
Images, recordings and documents sent for labelling often contain people, customers or trade secrets. We treat them that way from the first file.
Ask us about security- 01
Vetted, bound annotators
Annotators are background-checked and sign confidentiality agreements, and only the people assigned to your project can open its data.
- 02
Encryption and access control
Data is encrypted in transit and at rest, and labelling tools sit behind individual accounts with no bulk download.
- 03
Redaction before labelling
Faces, number plates, names and other personal details can be blurred or removed before annotation where the label does not need them.
- 04
Your platform, or deleted after delivery
We can work inside your own labelling platform so data never leaves your account. Otherwise, copies are deleted once you accept the delivery.
where this fits
Labelling sits between collection and analysis. Raw rows in, labelled rows out.
Labels turn collected data into something a model or an analyst can learn from. We can gather the data before labelling and analyse it after, or work with data you already have.
- stage.01 · collectout → raw rows
Data collection & web scraping
Exports from your own systems, plus public or partner sources gathered into clean, structured rows.
- stage.02 · labelyou are hereout → labelled rows
Annotation & labelling
People tag text, images and records to an agreed guide, and a second reviewer checks the hard cases.
- stage.03 · analyseout → findings
Data analytics
We clean and profile the data, find what actually predicts the outcome and spot gaps before anything is built on it.
- stage.04 · reportout → dashboards
Business intelligence
Dashboards your managers will open, showing the numbers the work is meant to move.
FAQ
Questions about Data Annotation & Labelling
Straight answers to what buyers ask us most. Can't find what you need?
Email sales@appsin.ai or call +91-9025555584.
Ask us directlyWhat is data annotation?
Data annotation is adding labels to raw data so a machine learning model can learn from it: boxes around objects in images, tags on words in text, transcripts for audio. The labels show the model the right answer for each example.
Which types of annotation do you offer?
Image classification, bounding boxes, semantic and instance segmentation, keypoints, video object tracking, text classification, named entity recognition, intent and sentiment labelling, audio transcription, speaker identification, captioning and rating LLM responses.
How do you make sure the labels are accurate?
Through written guidelines with examples, a pilot batch you approve, second-pass review, agreement checks where two annotators label the same item, and gold-standard samples with known answers mixed into each batch. Each delivery comes with a quality report.
Do you use AI to pre-label data?
Where it saves time, yes. A model proposes labels and a person checks and corrects every one, so the speed comes from the model and the accuracy from the review. We measure pre-labelled batches against the gold set like any other, and switch it off for tasks where correcting takes longer than labelling.
How long does an annotation project take?
Scoping and guidelines take about two weeks, and the pilot one to two more. After that, throughput depends on the data type, label complexity and quality level, and the quote sets a delivery schedule by batch.
Which tools and output formats do you use?
We commonly use CVAT and Label Studio, and can work in your own platform. Labels are delivered as COCO, YOLO, Pascal VOC, JSON, CSV or a custom schema that loads straight into your training pipeline.
Can you label data for large language models?
Yes. We rate and rank model responses, write and correct reference answers, and label intent and entities, producing data for fine-tuning, preference training and evaluation sets.
How is sensitive or personal data handled?
Annotators work under confidentiality agreements with individual accounts, data is encrypted, and personal details can be redacted before labelling. We can also work inside your own environment so the data never leaves it.
How is annotation priced?
It depends on the data type, label complexity, volume and quality level, so prices are set per item or per hour after we see sample data. The pilot batch shows the real effort, and we quote a fixed price for the full set from it.
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Data Annotation & Labelling brochure
Features, outcomes, delivery process and FAQs in one PDF you can share with your team or management.
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Keep exploring
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- Model Training & Fine-TuningTrain or fine-tune models on your labelled data, with evaluation sets to measure the result.
- AI project cost estimatorA quick estimate of what your AI project, including data preparation, could cost.
- AI & ML DevelopmentEnd-to-end AI and machine learning development, from a feasibility check and proof of concept to models running inside your ERP, CRM or apps.
- Generative AI DevelopmentGenerative AI applications that draft, summarise, search and answer from your own content, built on the model that fits and tested before rollout.
- AI Agent DevelopmentAI agents that read, decide and act across your systems, completing multi-step tasks with human approval where it matters and a log of every step.
- AI Chatbot DevelopmentCustomer-facing AI chatbots on your website, WhatsApp and app that answer from your content, complete simple tasks and hand difficult conversations to your team.
- ChatGPT & OpenAI IntegrationOpenAI models built into your apps, SaaS products and internal tools, with retrieval over your data, tested prompts and running costs you can plan for.
- LLM DevelopmentWe select, adapt and deploy large language models for your tasks, from fine-tuned open-source models on your servers to integrations with hosted APIs.
Further reading
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Start with a pilot batch
Send us a sample of your data and the labels you need. We will draft the guidelines, label a pilot set for your review and quote the full volume.
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