Text data collection
Domain documents, prompts, Q&A, conversations, search queries, handwriting and other task-specific text.
Create custom datasets
Purpose-built data. Collected for your model.
Scope and execute custom text, speech, image, video and multimodal collection with controlled participants, environments, metadata, consent and quality requirements.
Collection protocolpilot draft
01 / ModalityWhat should the model learn from?
Protocol completenessin draft
Modality-awareCapture the right signal
Participant-fitRecruit for the brief
Consent-awareKeep provenance attached
Built around the real work
Capabilities that move a model from promising to useful.
Choose the parts of the data and quality workflow that match the behavior, market or modality you are building for.
BUILD
Designed for the next decision, not the last task.
Every capability is shaped around downstream use: what the model needs to learn, what reviewers need to judge and what your team needs to ship with confidence.
Talk through your workflow01
Speech & audio collection
Scripted speech, spontaneous dialogue, wake words, commands, emotional speech and acoustic variation.
Image collection
People, objects, documents, products and environments captured to specified devices, angles and lighting.
Video collection
Actions, gestures, interactions, instruction-following, POV scenarios and time-based events.
Multimodal collection
Linked audio-video-text, image-text, sensor-linked or scenario-based datasets with shared context.
Synthetic + human validation
Generate candidate examples programmatically, then validate quality, diversity, realism and guideline compliance.
Dataset manifestSchema attached
collection_manifest.jsondraft
asset_idcaptured
participant_contextcaptured
environmentcaptured
consent_statuscaptured
qa_statusreview required
Capture stays reproducible.Raw files, metadata and acceptance rules travel together.
The specification is the product
Design the dataset around the deployment environment.
Real-world performance depends on more than sample count. Variation in users, devices, contexts, noise, lighting, language and behavior should be intentional.
- Country, locale, language, dialect and accent
- Device, operating system, acoustic and scene conditions
- Metadata, consent, rights, retention and access rules
The operating model
Pilot early. Scale only after the protocol works.
The collection process adapts to the risk and capture requirements of the data.
Scope
Define model goal, target population, modality, environment, metadata and privacy constraints.
Design pilot
Create instructions, examples, acceptance tests and a representative pilot batch.
Calibrate
Review pilot results, fix ambiguity and verify the data is useful downstream.
Scale
Recruit, collect, monitor progress and enforce automated and human quality gates.
Deliver
Package data with manifests and QA notes, then shape future collection from model results.
Quality that holds up in production
Move quality control closer to capture.
Catch corrupted files, duplicates, instruction failures, weak authenticity and coverage gaps while they are still cheap to correct.
- Pilot calibration and capture-format validation
- Completeness, duplicate and metadata checks
- Reject / rework rules, sampling QA and manifest reconciliation
SourceRaw files, participant or environment criteria and capture context
StructureStable IDs, filenames, split membership and dataset schema
QualityValidation status, issue tags, reviewer decisions and rework history
RightsConsent, provenance, retention and access records where applicable
What arrives at the end
Deliverables your team can use.
Clear outputs, stable structure and the context needed to move the work into training, evaluation or production.
01Asset manifest
02Metadata schema
03Raw and validated files
04Optional annotations, transcripts or captions
05Quality flags and QA report
06Consent and provenance records where applicable
07Known limitations
08Client-schema delivery mapping
Where it fits
Speech in the wild
Vary accents, devices, noise and environments deliberately.
Vision systems
Capture people, objects and scenes across lighting, angle and motion.
Multimodal interaction
Keep audio, video, text and context synchronized around a scenario.
Synthetic validation
Check generated examples for diversity, realism and guideline compliance.
Start with the outcome
Describe the dataset you wish existed.
We’ll turn the model goal into a collection protocol: participants, scenarios, devices, environments, metadata, QA and delivery structure.