Language models & NLP
Clean entity, intent and classification labels are the difference between a model that understands and one that guesses. We review every batch for taxonomy consistency.
Annotation · Sector
Annotation for text, voice & documents.
Language and speech models need structured, consistent ground truth. We label the text, audio and documents that teach models to understand entities, intent and sentiment — and, increasingly, the retrieval, reranking and tool-use data behind modern LLM and agent applications.
As the field moves from classic RAG toward agentic retrieval and context engineering, the data work shifts with it: relevance judgments for retrievers and rerankers, function-call traces for tool-using agents, preference pairs, and evaluation sets. We handle the review-heavy, taxonomy-consistent labelling all of that depends on — multilingual-capable, with a QA pass on every batch.
Annotation types
What the work looks like.
Text & NLP labelling
Named-entity recognition, intent and sentiment, text classification, relation extraction — the building blocks of language understanding.
LLM & retrieval data
Retrieval relevance and reranking judgments, tool-use / function-call traces, and preference pairs for tuning and evaluation.
Audio & speech
Transcription, speaker diarization, and intent tagging for voice-assistant, call-analytics and speech models.
Documents
Layout, field and OCR-correction labelling that turns messy documents into structured training data for document-AI pipelines.
Coverage
What we annotate.
Named-entity recognition
Intent & sentiment
Text classification
Relevance & reranking
Tool-use traces
Transcription
Speaker diarization
Document layout · PII redaction
Where it's used
How it's used.
LLMs, RAG & agents
As retrieval becomes agentic and context-engineered, the eval and relevance data behind it becomes the moat — we produce the review-driven judgments that make it trustworthy.
Voice & documents
Transcription, diarization and document labelling feed voice assistants, call-analytics and document-AI — capturing who spoke, what they meant, and what a page contains.
Multilingual-capable and growing — a good fit for teams building LLM, RAG, chatbot, voice-assistant or document-AI products who need a review-driven labelling partner.
