Text, image, video, audio, multimodal
Data labeling services
Scope data labeling services with rules, reviewers, and acceptance defined first.
Move datasets from raw to ready with label rules, sample examples, reviewer checks, and acceptance criteria recorded in writing before any annotation begins.
Upload files for a quote
Short form: name, work email, data type, locale notes, and sample files or links if ready.
Coverage reviewed by dataset
Standard for a 5,000-row text labeling pass
Confidentiality controls before file transfer
Dynamic Dialects supports requests across 250+ languages with ISO 9001/27001 operating controls, ISO 17100 applied to translation scopes, 40,000+ vetted linguists, named project coordination, and written confirmation before production work begins.
Evidence for review
What DD can show before a buyer commits.
This is not a public case study claim. It is DD-owned evidence a buyer can request when the work needs vendor review before a scope is approved.
Ask for proof details- Buyer type
- Data labeling services buyer, vendor manager, or operations lead qualifying DD before sending a live requirement.
- Problem
- The buyer needs scope data labeling services with rules, reviewers, and acceptance defined first. scoped by files, audience, language pair, deadline, recipient rules, and review process before quote approval.
- Scope
- Data labeling services work coordinated by DD with written request review, named PM ownership, and review records matched to the request type.
- Constraint
- This page cannot rely on a public case study yet; it must point to DD-owned proof artifacts and disclosure-safe process evidence.
- DD action
- DD confirms the inputs, missing details, staffing option, quality check, and delivery record before production work begins.
- Evidence available
- Private proof can include a request-specific checklist, redacted QA summary format, delivery record format, and sourcing or reviewer notes.
- Outcome
- The buyer can judge whether DD fits the requirement before sending production files or adding this service to a vendor shortlist.
- Disclosure status
- DD-owned proof only. Public outcomes require client approval; redacted process artifacts can be shared when terms allow.
How the work runs
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Scope rules
Label categories, examples per class, and acceptance criteria recorded in writing before any annotation work begins.
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Calibrate on samples
A small sample run through the rules first, with results checked against your expectation. Rule-set ambiguity gets caught in 50 rows rather than 5,000.
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Label in batches
Bulk labeling in tracked batches with reviewer instructions, progress visible per language pair, and notes recorded for edge cases as they appear.
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Trace reviewers
Each row carries the reviewer name, the label applied, and any notes attached. Flagged rows trace back to a named source, not to a generic team.
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Confirm acceptance
Final output tied to the acceptance criteria set at step 1, with a per-class summary and a list of flagged rows ready for your downstream pipeline.
Each data labeling project starts with a written request check confirming dataset modality (text, image, video, audio, multimodal), label categories, sample rows, reviewer instructions, acceptance criteria, output format, and confidentiality controls. Sample rows are calibrated against your rule set before bulk labeling begins, not afterward. Reviewer outputs are tracked per row so quality questions trace back to a named decision rather than a generic team. Standard turnaround for a 5,000-row text labeling pass is 5–7 working days; image, video, and audio cadence depends on annotation type and reviewer load and is quoted with a confirmed delivery date in writing.
For annotation work, DD checks label definitions, examples, sample review needs, and output format before quoting.
What this page helps you send
- Text labeling: named entity recognition, intent classification, sentiment, topic, toxicity, span extraction.
- Image labeling: bounding boxes, polygons, semantic segmentation, landmark points, classification.
- Video labeling: frame-level annotation, object tracking, event detection, action recognition.
- Audio labeling: speaker diarization, sound event tagging, transcript segmentation, prosody marks.
- Multimodal labeling: image–text pairs, video–caption alignment, screenshot–intent matching.
- Multilingual labeling across 250+ languages with reviewer-checked spelling, script, and locale notes.
- Pre-existing label rule sets translated and applied to non-English data with consistency notes.
- Rare-language and dialect-sensitive datasets where most marketplaces cannot source qualified reviewers.
What you receive
- Labeled dataset in the requested output format (JSONL, CSV, COCO, YOLO, or platform-specific export).
- Sample-row calibration report showing rule application before bulk work began.
- Per-row reviewer trace so any flagged label is attributable to a named decision.
- Reviewer notes for edge cases, ambiguous rows, and label-rule clarifications added during the work.
- Acceptance summary tied to the criteria recorded at the start, not invented after the fact.
Questions teams ask first
What dataset modalities are handled?
Text, image, video, audio, and multimodal datasets. Within text: named entity recognition, intent and topic classification, sentiment and toxicity, span extraction, and rewriting. Within image and video: bounding boxes, polygons, semantic segmentation, landmark points, object tracking, and event detection. Within audio: speaker diarization, sound event tagging, transcript segmentation, and prosody marks. Multimodal pairings (image–text, video–caption, screenshot–intent) are scoped per project.
What does the request check confirm before labeling starts?
Dataset modality, language coverage, label categories with examples per class, reviewer instructions, acceptance criteria, output file format, and confidentiality controls. Sample rows are run through the rule set first and the results are checked against your expectation before bulk labeling begins. This catches rule-set ambiguity in 50 rows rather than 5,000.
How are reviewer outputs tracked?
Per-row trace. Each labeled row carries the reviewer name (or anonymized ID), the label decision, and any notes attached during the work. When a flagged row needs a second look, the original decision is visible and the reviewer can be brought back to the question rather than starting from scratch.
How long does a labeling pass take?
Standard turnaround for a 5,000-row text labeling pass is 5–7 working days from receipt of the rule set and a clean dataset. Image, video, and audio cadence depends on annotation type (bounding box vs polygon vs segmentation, for example) and reviewer load. Every project is quoted with a confirmed delivery date in writing rather than a vague estimate.
How is confidentiality handled for proprietary datasets?
An NDA is signed before any file transfer when requested. Files are kept on access-restricted storage, named-reviewer handling is available for sensitive datasets, and files are deleted on a defined schedule after project close. Reviewer access scopes can be aligned with your security posture on request.
Can rare-language or dialect-sensitive datasets be handled?
Yes. Coverage spans 250+ languages, including rare pairs where most data-labeling marketplaces cannot source qualified reviewers. For dialect-sensitive work (Levantine vs Gulf Arabic, Brazilian vs European Portuguese, Mandarin vs Cantonese, etc.) the dialect target is confirmed in the request check and reviewers are matched to it.
Can existing rule sets be reused on new data?
Yes. Send the prior rule set, prior examples, and any glossary. The rule set is applied to your new data with consistency notes added for any edge case that the original rules do not cover. If the dataset is multilingual, the rule set is translated and adapted per language so reviewers in each language work from the same definitions.
What output formats are supported?
JSONL, CSV, COCO, YOLO, Pascal VOC, and platform-specific exports (Label Studio, Labelbox, Scale AI, SuperAnnotate, V7, and others on request). Output format is confirmed during the request check so the labeled dataset drops into your downstream pipeline without a separate transformation step.