Sentiment analysis
Polarity and emotion labels on reviews, social posts and support tickets.
Text annotation for NLP and LLMs
Turn unstructured text into AI-ready data with precise annotation, from sentiment tagging to entity recognition.
- NER · sentiment · classification
- Native-speaker annotators
- 98%+ accuracy guarantee
support_tickets_batch_07.jsonlannotation
Support ticket #48213
Hi, this is Maria ChenPERSON. I ordered the AeroFit running shoesPRODUCT from Northwind OutfittersORG on March 3DATE and was charged $129.99MONEY twice. The parcel is still stuck in RotterdamLOC. Please refund the duplicate charge.
- intent: refund_request
- sentiment: negative
- urgency: high
- topic: billing · shipping
Export · JSONL
{
"id": "48213",
"entities": [
{ "text": "Maria Chen", "label": "PERSON" },
{ "text": "AeroFit running shoes", "label": "PRODUCT" },
{ "text": "Northwind Outfitters", "label": "ORG" },
{ "text": "March 3", "label": "DATE" },
{ "text": "$129.99", "label": "MONEY" },
{ "text": "Rotterdam", "label": "LOC" }
],
"intent": "refund_request",
"sentiment": "negative"
}illustrative examplesample data
Text annotation types
Linguistically trained annotators
Our linguistically trained workforce labels everything from social media comments to clinical notes. Sentiment, named entities or intent, we help your models read between the lines with confidence.
Named entity recognition
People, organizations, dates, products and domain-specific entities tagged in context.
Audio & text transcription
Accurate transcripts of recordings and of handwritten or scanned text, ready for training.
OCR
Text boxed and transcribed on documents, forms and receipts, with field-level labels for extraction.
Natural language processing
Intent, topic and document classification, plus part-of-speech and relation labeling.
Text data collection
Domain-specific and multilingual text sourced to fill gaps in your corpus.
From ticket to training data
Structure hidden in everyday language
Annotators tag entities, intent, sentiment and urgency in the same pass, then export clean records your NLP pipeline can use directly.
- Nested and overlapping entities
- Domain glossaries for legal, medical and finance text
- Inter-annotator agreement checks on ambiguous spans
Support ticket #48213
Hi, this is Maria ChenPERSON. I ordered the AeroFit running shoesPRODUCT from Northwind OutfittersORG on March 3DATE and was charged $129.99MONEY twice. The parcel is still stuck in RotterdamLOC. Please refund the duplicate charge.
- intent: refund_request
- sentiment: negative
- urgency: high
- topic: billing · shipping
Export · JSONL
{
"id": "48213",
"entities": [
{ "text": "Maria Chen", "label": "PERSON" },
{ "text": "AeroFit running shoes", "label": "PRODUCT" },
{ "text": "Northwind Outfitters", "label": "ORG" },
{ "text": "March 3", "label": "DATE" },
{ "text": "$129.99", "label": "MONEY" },
{ "text": "Rotterdam", "label": "LOC" }
],
"intent": "refund_request",
"sentiment": "negative"
}Use cases
Where teams put this data to work
Customer support automation
Intent, urgency and entity labels to route tickets and train support bots.
Document processing
Field extraction from invoices, contracts and forms, combined with OCR.
Search & recommendations
Query intent and product attribute tagging for e-commerce search.
Social & review analytics
Sentiment, topic and aspect labels across reviews and social posts.
How we work
From pilot to production
We start with your goals and domain, apply consistent tagging with tailored guidelines and tooling, and run multiple quality checks with your feedback built in, so every dataset is clean, consistent and model-ready.
- Step 01
Consult
We review your data, edge cases and success criteria, then write annotation guidelines with you.
- Step 02
Annotate
A trained team labels a pilot batch, following the agreed guidelines to the letter.
- Step 03
Calibrate
You review the pilot. We fold your feedback into the guidelines before scaling up.
- Step 04
Multi-layer QA
Every batch passes peer review, expert QA and automated checks before it leaves us.
- Step 05
Deliver
Labels ship in your format on your schedule, ready to drop into training.
Quality pipeline
Five checkpoints between raw data and your model
98%+
Accuracy guarantee
- 01AnnotatorLabels against your guidelines
- 02Peer reviewA second annotator reviews the work
- 03Expert QASenior reviewer samples and corrects
- 04Automated checksRules catch missing or invalid labels
- 05Client sign-offYou approve before final delivery
Results
Quality you can measure
- Accuracy guarantee
- 98%+Accuracy guarantee
- Returning clients
- 96%Returning clients
- Cost reduction
- 2.5xCost reduction
- Projects completed
- 200+Projects completed
“Intellisane AI played a key role in helping us reach 97% accuracy in automating foundation layout detection. Their deep understanding of spatial data and labeling precision brought measurable improvements to our AI pipeline. The team was communicative, detail-oriented, and delivered everything ahead of schedule.”
S. Ragavan
Sr. ML Engineer, Taranto
Related
Helping AI understand human language across e-commerce, law, healthcare, finance, education and more.
FAQ
Questions, answered
Something else on your mind? Email sales@intellisane.ai.
Get started
Let's scope your project
Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.