Multilingual data problems in AI programs are usually not translation problems. Labels drift by language when annotators apply instructions differently across locales. Speech files need language-context review, not just transcription. Model outputs need linguist evaluation, not generic QA. When those distinctions are not built into the workflow, datasets that look clean at batch level can still create model-quality gaps.
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 detailsDD structures AI data engagements around a separation-of-roles principle at every stage: the person who annotates a record is not the person who reviews that record. Annotators self-check against schema guidelines; a senior reviewer samples the batch and checks cross-annotator consistency; the PM validates batch-level completeness and schema compliance before each release. That chain is documented, not verbal - and it applies at every delivery, not only the final one.
The request review review separates the work type before quoting. Annotation, evaluation, transcription, and dataset cleanup each carry different task rules, different output schemas, and different quality-check criteria. DD uses the task type, sample file, language list, and the quality check the buyer will apply to decide whether the returned data is acceptable - to separate these before accepting a project, not after reviewing the first batch.
Cross-annotator consistency is tracked on all annotation and evaluation projects. If annotators for the same language are applying label categories differently, that is flagged before the batch is released - not discovered when the training run produces unexpected behavior. Unclear examples, ambiguous label cases, and instruction edge cases are documented and returned with the batch, not silently forced into a label.
For AI data pipelines that deliver in rolling batches: DD can receive content incrementally and return processed output on a defined cadence. Batch-level quality metrics - completeness, schema compliance, cross-annotator consistency - are reported at each delivery, not accumulated and reported at project close. That gives data ops teams the signal they need to catch systematic issues early.
Human-only workflows are available where the engagement prohibits AI-assisted production. For projects where the training-data integrity requires full human annotation with no AI-assisted labeling, DD delivers that. AI policy is client-configurable at request review. For AI-assisted workflows, all AI output is reviewed by a human linguist before delivery.
Cross-annotator agreement, schema compliance, and completeness reported at every batch — not accumulated to project close.
Step by step
Send the task type (annotation, evaluation, transcription, cleanup), a sample data file, language list, label rules, output schema, volume, and the quality check your team will apply to decide batch acceptance.
DD reviews the task type, sample, label format, and your acceptance criteria before accepting the project. Annotation, evaluation, transcription, and cleanup each carry different quality-check criteria — these are confirmed before quoting, not assumed.
Annotators self-check against schema guidelines. A senior reviewer samples the batch and checks cross-annotator consistency. The PM validates batch-level completeness and schema compliance before any release.
Each delivery includes batch-level quality metrics: completeness, schema compliance, and cross-annotator consistency. Ambiguous label cases and instruction edge cases are documented and returned with the batch.
The annotator is not the reviewer. Annotators self-check against schema guidelines. A senior reviewer samples the batch and checks cross-annotator consistency. The PM validates completeness and schema compliance before each release.
Cross-annotator consistency, schema compliance, and completeness metrics are reported at each batch delivery — not accumulated and reported only at project close. Systematic issues are caught early, not at model training.
Unclear examples, ambiguous label cases, and instruction edge cases are documented and returned with the batch. Items that cannot be cleanly labeled are kept separate rather than silently forced into a category.
Content can be received incrementally and returned processed on a defined cadence. Schema, label format, and output requirements are matched to the downstream pipeline specification agreed at request review.
Quality stages
The task type, task rules, sample record, and the quality check the buyer will apply to decide batch acceptance are all confirmed before production begins — preventing the most common AI data rejection.
Annotators work against confirmed schema guidelines and self-check their own records before submission. Unclear examples are flagged rather than forced into a label.
A senior reviewer independently samples the batch, checks for cross-annotator consistency, and flags any systematic label drift before the batch is released.
The PM validates batch-level completeness and schema compliance. Quality metrics are documented and delivered with the batch, not accumulated and reported only at project close.
Send task type, sample file, language list, label rules, output schema, volume, and your quality-check criteria. DD returns scope, PM assignment, and a delivery plan before work begins.
DD separates annotation, review, and PM validation. Annotators self-check against schema rules; a senior reviewer samples consistency; the PM validates completeness and schema compliance before release.
Consistency is checked at batch level before release. If annotators apply label categories differently, DD flags and resolves the issue before the batch ships.
Yes. DD can receive content incrementally and return processed output on a defined cadence. Batch-level quality metrics - completeness, schema compliance, cross-annotator consistency - are reported at each delivery.
Ambiguous examples and instruction edge cases are documented and returned with the batch, not silently forced into a label.
Yes. For projects where training-data integrity requires full human annotation with no AI-assisted labeling, DD delivers that. AI policy is client-configurable at request review.
Send task type and a sample file, language list, label rules and examples, output schema, volume, delivery cadence, tooling constraints, and the quality check your team will apply to decide whether the returned data is acceptable.
Share the language pair, file type, audience, or problem. DD replies with availability, open questions, handling notes, and the next step before work starts.
