Record where material came from and the consent or license required for its intended use.
Speech, Text and Image Data Collection for AI
Collect the right multilingual data before you scale the wrong dataset.
Source and prepare multilingual speech, text, image or audio data against the language, provenance, rights and schema requirements of a specific model use.
Ask for a pilot manifest with source class, permitted use, language or dialect, schema result and rejection reasons.
110,000+
verified language specialists
· Counted from our linguist database · verified June 2026
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served since 2015
Dataset readiness decision
What makes a collected record usable for this model?
A large dataset is not a result if its rights, regional label or schema cannot be trusted. These checks make the pilot an acceptance decision before volume.
Sample regional variants and scripts instead of treating a broad language name as enough.
Test files, metadata and exclusions against the fields the model team needs.
Return duplicates, missing provenance and unusable records with a reason and correction owner.
Provenance visiblePilot before volumeRejections explained
Scope dossier
Speech, Text and Image Data Collection for AI service fit Ask for a pilot manifest with source class, permitted use, language or dialect, schema result and rejection reasons.- Typical inputs
- Target languages and dialects, modality, intended model use, source restrictions, consent or license requirements and target schema
- Controls
- Provenance record, consent or license check against the actual source, duplicate review, language validation and rejection reasons
- Best fit
- Acquiring and curating source material before annotation or a wider model-ready training-data program
What AI data collection involves
The source and its permitted use are part of the dataset.
Multilingual AI data collection gathers speech, text, image or audio material for a defined model use and records the information needed to judge whether each record belongs in the dataset. The job is not simply to deliver more files. Source rights, contributor consent where needed, regional language labels, duplicates, schema and unusable samples determine whether the model team can use the material at all. MoniSa agrees these rules with the buyer, tests them on a pilot set and returns rejection reasons before scaling. Annotation of an existing source set and the wider training-data program are related but separately scoped work.
Service signal
Pick the service by the result at risk.
Buyers can see the result, review depth, and file-shape fit before they compare vendors line by line.
When to use it
When a large file drop would be unusable because source rights, regional variants, duplicates or output schema were left open.
Strongest fit
Acquiring and curating source material before annotation or a wider model-ready training-data program
How the work runs
Agree a source and rights brief, define the collection spec, review a pilot against acceptance rules, then scale in scheduled batches
Formats we handle
AudioSpeech and voiceover
TextDocuments, UI, copy
ImageStills and scans
MetadataTags and taxonomy
Who this is for
Each stakeholder sees their risk.
Buyers need to see when the service fits, what can go wrong, and how review reduces rework.
AI data lead
Needs usable source material in the specified languages, modalities and schema.
Data rights owner
Needs source provenance and permitted use checked against the actual dataset plan.
Model engineer
Needs rejection reasons and variant labels before accepting a pilot for scale.
Specification
Lock the details that decide quality.
Use this table to compare inputs, review model, fit, and output before a buying committee asks.
| Typical inputs | Target languages and dialects, modality, intended model use, source restrictions, consent or license requirements and target schema |
|---|---|
| Review path | Provenance record, consent or license check against the actual source, duplicate review, language validation and rejection reasons |
| Strongest fit | Acquiring and curating source material before annotation or a wider model-ready training-data program |
| How the work runs | Agree a source and rights brief, define the collection spec, review a pilot against acceptance rules, then scale in scheduled batches |
Pilot controls
Test source rights and usability before collection scales.
The AI data owner should approve permitted sources, target variants, schema and rejection rules on a pilot. Rights clearance depends on the actual source and intended use.
Rights brief
Name permitted sources, consent or license requirements and use restrictions.
Language spec
Set language, dialect, modality, format and metadata rules.
Pilot
Collect a small representative set against written acceptance criteria.
Inspect
Check provenance, duplicate records, schema fit and language mismatch.
Reject
Record why unusable items failed and who owns a correction.
Scale decision
Agree whether the pilot supports a larger collection plan.
Buyer proof request
Ask for a pilot manifest
Check source class, permitted use, language or dialect, schema result, rejection reason and correction owner on a real pilot before deciding whether to scale collection.
Related dataset decisions
Carry the source brief into the next data step.
Collection and curation settle rights, language fit and usable format. The next route depends on whether the buyer needs labels or a wider training-data program.
AI training data services
Plan creation, preparation and model-ready dataset delivery beyond collection.
Data annotation services
Apply labels once the source set and its acceptance rules are known.
Data rights and provenance checklist
Specify the source and permitted-use evidence a pilot should carry.
AI data services
Return to the full AI data workstream.
Buyer questions
Answers in writing, before you ask for a call.
The questions buyers send before a scope conversation, answered on the page rather than in a meeting. Take them to your team, then send us the one we did not answer.
What comes before multilingual data collection?
Define the intended model use, permitted source classes, language variants, consent or license requirements and target schema.
How can a buyer assess a pilot?
Inspect a manifest that records source class, permitted use, language or dialect, schema result and rejection reason for sampled items.
Does every collected record carry the same rights?
No. Permitted use depends on the actual source, consent or license and intended model use; these must be checked for the project.
Data collection brief
Define the dataset before asking for volume.
Describe the model use, permitted source classes and schema outline. Arrange transfer of any source files or detailed schema after a project data-handling path is agreed.
Send a brief