Annotator pool qualified per task type, includes rare and accented pairs
AI data services
Scope AI data services with task type, language coverage, and reviewer rubric settled first.
Deliver task-specific AI data sets (training data, evaluation prompts, red-team prompts, RLHF preference pairs, annotated examples) in the agreed languages with rubric, calibration, and reviewer-grade QA confirmed in writing before any annotator opens a sample.
Upload files for a quote
Short form: name, work email, data type, locale notes, and sample files or links if ready.
Training data, eval sets, red-team, RLHF, instruction-tuning
Annotator alignment target recorded per program with calibration sample set
JSONL, Parquet, CSV with schema delivered alongside the data
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
- AI data services buyer, vendor manager, or operations lead qualifying DD before sending a live requirement.
- Problem
- The buyer needs scope ai data services with task type, language coverage, and reviewer rubric settled first. scoped by files, audience, language pair, deadline, recipient rules, and review process before quote approval.
- Scope
- AI data 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 the program
Task type, target languages with regional variants, reviewer rubric, IAA target, and delivery format settled in writing first.
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Calibrate the rubric
Calibration sample set scored by reviewers against the rubric. Annotators work the calibration set before production and the IAA score is recorded.
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Annotate against the rubric
Native-language annotators work in-tool with the calibrated rubric and edge-case guidance. Borderline cases flagged for reviewer adjudication.
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Run reviewer-grade QA
Reviewer-grade QA on every sample band before release. Annotators who fall below the IAA floor are recalibrated or replaced.
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Deliver and archive
Data set delivered in JSONL, Parquet, or CSV with schema. Calibration report, edge-case log, and license records archived for compliance review.
Each AI data program starts with a written specification confirming task type (multilingual training data generation, evaluation set creation, red-team prompt sourcing, RLHF preference data, instruction-tuning data, supervised fine-tuning examples), target languages and locales (with regional variant per language), reviewer rubric and edge-case guidance, calibration sample set with reviewer alignment scored before production starts, annotator pool qualification per language, IAA score target, and delivery format (JSONL, Parquet, CSV with schema). Annotators work against the calibrated rubric with reviewer-level QA on every sample band before the data set is released.
For annotation work, DD checks label definitions, examples, sample review needs, and output format before quoting.
What this page helps you send
- Multilingual training data sourcing and writing for LLM pretraining and fine-tuning across 250+ languages.
- Evaluation set creation with verified reference answers, edge-case prompts, and reviewer-graded scoring.
- Red-team prompt sourcing across safety, jailbreak, prompt-injection, and harmful-output categories per language and locale.
- RLHF and DPO preference data with paired completions, ranking rationale, and annotator alignment.
- Instruction-tuning examples and supervised fine-tuning (SFT) data with reviewer-graded sample quality.
What you receive
- Annotated data set in the agreed format and schema with reviewer-grade QA per sample band.
- Calibration set report with IAA scores and annotator alignment notes.
- Edge-case sample log with rubric notes and reviewer rationale on borderline cases.
- License and consent records archived for compliance review on request.
- Delta updates against the same rubric and annotator pool for subsequent program rounds.
Questions teams ask first
What task types are supported?
Multilingual training data generation, evaluation set creation, red-team prompt sourcing (safety, jailbreak, prompt-injection, harmful-output), RLHF and DPO preference data, instruction-tuning examples, and supervised fine-tuning data are supported. The task type is confirmed during scoping so annotators with the matching qualification and reviewer rubric are assigned.
How is rubric calibration handled?
Each program starts with a calibration sample set scored by reviewers against the rubric, with edge-case notes added. Annotators work the calibration set before production starts and the IAA score is recorded. Annotators who score below the agreed IAA floor are recalibrated or replaced before production data is released.
How are red-team prompts sourced per language?
Red-team prompt categories (jailbreak attempts, harmful-output elicitation, prompt-injection patterns, multilingual safety bypass attempts) are sourced by native annotators per language and locale. Locale-specific cultural attack vectors are scoped per market rather than translated from an English source set.
What delivery formats are supported?
Standard delivery formats include JSONL with field-level schema, Parquet for large data sets, and CSV with explicit schema. Schema fields are confirmed in scoping (sample ID, locale, task type, reference answer, annotator ID, IAA band, calibration tier) so the data set drops directly into the model training pipeline.
How is consent and license handled?
Source content consent and license terms are confirmed in scoping per content origin (annotator-written, user-generated with consent, public-domain, proprietary licensed). Records are timestamped and archived for compliance review. Annotator-written content is licensed per the annotator contract; user-generated content requires consent timestamped per record.