AI data annotation

Scope multi-modal AI annotation before labels enter production.

DD reviews modality mix, shared categories, examples, privacy terms, and sample acceptance before proposing a labeling plan.

Discuss scope and secure transfer

Tell us the file-kind mix and the locale notes, and add a sample link when you have one.

Scope Before quote

Requirements and feasibility checked for this request

Review Agreed method

Acceptance and ownership confirmed in writing

Dynamic Dialects reviews requests across 250+ languages. DD holds ISO 9001:2015 and ISO/IEC 27001:2022 certifications. It works with 40,000+ vetted linguists. The written scope confirms staffing, coordination, and review controls for your request.

What DD checks before quoting

Modality mix Which mix of text, image, video, and audio enters the same programme.
Shared taxonomy Categories that must mean the same thing across modalities, with worked examples.
Pilot batch A small mixed sample used to expose rule gaps before production labeling.
Unified export How records from each modality join one schema the buyer can load.

Example scope — actual checks vary by project type and client requirements.

Illustrative DD scope worksheet

What this request needs to settle

The buyer decides whether one shared taxonomy can cover text, image, video, and audio without silent meaning drift. Annotation across modalities is not one generic queue. DD needs the modality mix, shared categories, examples per type, disagreement owner, privacy limits, and export schema. A mixed pilot exposes rule gaps before production labeling.

Illustrative multi-modal taxonomy pilot, not a client dataset

Illustrative request, not a client case or a promised result.

A product team wants the same intent labels on chat text, screenshot images, short clips, and call audio. A complaint that is clear in text can look ambiguous in a screenshot. This hypothetical conflict is not a DD accuracy or model-performance claim.

Inputs to check: The buyer supplies rights-cleared samples for each modality through an approved transfer method, shared category definitions, modality-specific examples, unknown-label rules, privacy limits, and export columns. Volume supports feasibility review, not a capacity claim.
Review decision: A mixed pilot returns ambiguous items with a reason code and buyer question instead of forcing a category. The buyer adjudicates cross-modality conflicts and updates the shared rule. Spot checks define what counts as disagreement and who resolves it.
Acceptance method: Sample output could contain record ID, modality, locale, label, uncertainty marker, rule version, reviewer note, and buyer disposition. The buyer checks schema and rule application on the agreed pilot; rejected records follow defined correction terms. No universal accuracy or model gain is implied.
Exception to resolve: If the buyer changes a category after the pilot starts, affected modalities and rule versions are identified before rework is scoped. If a modality cannot be shared under the approved transfer method, it stays outside the pilot rather than moving through an informal channel.
Record to request: Ask for a blank multi-modal label sheet with record ID, modality, rule version, category, ambiguity reason, owner answer, and disposition. It contains no DD client data.

Questions to settle before requesting a quote

Which modalities share one taxonomy?List text, image, video, and audio in scope so examples can be prepared for each.
Where do meanings drift across modalities?Provide edge cases that look different in text versus image, clip, or audio.
Who settles cross-modality disagreements?Name the buyer owner who updates the shared rule before production labeling.
What does an accepted export contain?Specify IDs, modality, labels, uncertainty, rule version, and required notes.

DD checks task, language, reviewer, and access feasibility for the modality mix. No fixed accuracy, dataset size, staffing level, or downstream model gain is claimed. The buyer supplies lawful access, policy decisions, and final acceptance authority.

DD reviews the request, checks availability and requirements, and confirms the proposed scope and handling method in writing before work starts.

On mixed-data labelling, DD looks at class definitions, worked examples, sample-check needs, and the return layout before a quote.

Where this service fits

Programmes that mix text, image, video, or audio under one taxonomy.
Pilots that must settle edge cases before production labeling.

What DD will confirm

A proposed multi-modal pilot and review scope.
Schema, ownership, and acceptance terms to approve.

Questions teams ask first

Can one taxonomy cover every modality?

Only if each category is defined with examples for every modality in scope. DD flags gaps during the pilot instead of assuming the same rule travels unchanged.

Can DD invent the shared taxonomy alone?

DD can draft a schema and expose gaps with examples; the buyer must approve definitions and decision authority in the scope.

What if one modality needs different categories?

Say so early. A split taxonomy can be safer than forcing one label set that does not travel.

Can personal data be pasted into the inquiry form?

Do not put sensitive records in the open form. Describe the task and agree access and handling before sharing samples.