Text & NLP services · 09 / 10
Text & NLP
Entities, intent, sentiment and relations, plus preference data for LLMs.
Language labelling is mostly about agreeing on definitions. We turn your taxonomy into a decision-tree guideline, measure agreement between annotators, and resolve every disagreement before it reaches your dataset.
Typical use cases
- Named entity recognition
- Intent and routing
- Document extraction
- LLM preference & RLHF ranking
- Content moderation
Sample deliverable
What the output looks like.
On 14 March 2026DATE3/3, Priya NairPERSON3/3 filed claim CLM-88213CLAIM_ID3/3 with Northwind MutualORG3/3 after a rear-end collisionEVENT2/3 on Ring Road, AhmedabadLOC3/3. The garage estimated repairs at ₹1,42,000AMOUNT3/3 and asked for the payout to be expedited.
Synthetic demonstration scene. No client data.
Variants
Span labelling
Entities and relations with character-level offsets.
Classification
Intent, sentiment, topic and toxicity at any granularity.
LLM evaluation
Pairwise preference, rubric scoring and response rewriting.
QA metric
Inter-annotator agreement
κ ≥ 0.80
Cohen’s / Fleiss’ kappa reported per label.
Export formats
- JSONL
- spaCy
- CoNLL
- Label Studio JSON
- CSV
How every project runs
- Scope call & sample review
- Written guideline & gold set
- Fixed-fee pilot with QA report
- Production in weekly batches
Next step
Scope your project with
an annotation lead.
A 30-minute call with the person who would run your project. We review a sample of your data, recommend an approach and send a fixed-price proposal within two business days.