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Data annotation

Turn raw data into structured datasets for AI/ML training. Precise labeling for text, images, audio, video, and user-generated content — across languages and domains.

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When Do I Need Data Annotation?

Train or Validate Models Fix Model Failures Build Gold Sets Evaluate LLMs
Build labeled datasets that teach your AI to recognize patterns, improve accuracy, and pass validation. Target weak spots — specific languages, genres, or user groups where your model falls short. Create gold-standard benchmarks to test model output, compare vendors, and set quality baselines. Score responses, rank the best outputs, and label failure reasons to fine-tune large language models.

How Data Annotation Works

Step 1: Scope

Define model goal, data type, languages, labels, and output format

Step 2: Guidelines

Create annotation guidelines with examples and edge cases

Step 3: Pilot

Run a small sample to measure speed and resolve ambiguity

Step 4: Annotate

Process production data in batches with full tracking

Step 5: QA

Reviewer pass, spot checks, gold set, IAA, and final audit

Step 6: Deliver

Receive dataset + quality report in your required format

What We Annotate

Metadata Creation

Make content searchable and AI-ready: language, topic, genre, speaker, platform, source, rights, sentiment, maturity rating, and custom taxonomy tags.

Classification & Topic Modeling

Classify by topic, intent, sentiment, or custom labels. Discover recurring themes and build taxonomies for analytics, moderation, or model training.

Transcription & Audio Labeling

Transcribe and label multilingual audio for ASR and voice AI. Add timestamps, speaker labels, emotions, intents, and QA checks.

Toxicity & Ethical Rewrite

Identify toxicity, bias, harassment, and safety risks across languages. Rewrite unsafe content into policy-compliant alternatives.

Deliverables

Ready-to-Use Datasets Guidelines & Gold Sets QA Reports Annotation Stats
Excel, CSV, JSON, XML, or any custom schema you need. Complete annotation guidelines and gold-standard reference sets. Error taxonomies, quality metrics, and compliance verification. Full statistics on annotation speed, accuracy, and inter-annotator agreement.

FAQ

Post-editing fixes AI output for immediate use. Annotation goes further — labeling and structuring data so it can train models, enable analytics, and improve future quality.

Text, images, audio, video, and user-generated content. We handle multilingual and multi-domain projects.

Excel, CSV, JSON, XML, spreadsheets, or any client-specific schema.

We help design the pilot and create guidelines during the scoping phase.

Multi-layer QA: reviewer pass, spot checks, gold sets, inter-annotator agreement (IAA), and final audit.

Yes. We identify toxicity, bias, harassment, and safety risks, and can rewrite content into policy-compliant alternatives.