Language data infrastructure for inclusive AI.
Mahder helps teams collect consented text and speech data through paid local contributors, reviewed workflows, and documented dataset delivery.
Inclusive AI requires more than scraped web data.
Low-resource languages need local context, consented participation, review, documentation, and fair compensation.
Data Scarcity
The vast majority of languages are underrepresented in digital spaces. Building capable models requires intentional, ground-up collection efforts rather than relying on sparse organic data.
Quality and Context Gaps
Machine translation and synthetic data often fail to capture dialectal nuance, cultural context, and natural speech patterns. High-quality data requires native speakers.
Ethical Collection Requirements
Sustainable AI infrastructure demands transparent consent, fair compensation for contributors, and rigorous review processes that respect local communities.
From local contribution to model-ready datasets.
Design collection plan
Define sampling requirements, demographics, and formatting rules.
Contributors complete tasks
Local participants record speech or write text via our mobile app.
Reviewers verify quality
Dedicated quality queues ensure data meets strict acceptance criteria.
Teams receive datasets
Access well-documented, clean data ready for model training.
Managed collection when off-the-shelf data is not enough.
Custom text collection
Targeted prompts, translations, and conversational text generated by native speakers.
Speech prompt recording
High-quality audio collection for ASR and TTS with diverse demographic sampling.
Contributor recruitment
Sourcing and onboarding verified local contributors for specific language needs.
Review & quality ops
Multi-pass validation pipelines to guarantee dataset integrity and accuracy.
Dataset packaging
Clean, structured, and fully documented data deliveries formatted for ML pipelines.
Human preference signals
RLHF and alignment data collected from culturally relevant evaluators.
An app for contributors.
A workflow for dataset teams.
Contributors join through the mobile app, review consent and task details, submit text or speech, and receive payout records for accepted work. Dataset teams manage tasks, sampling, review queues, and dataset handoff.
Open and custom datasets for low-resource languages.
Start with documented language resources, then request custom collection when your use case needs fresh coverage, stricter sampling, domain-specific data, or private handling.
Request accessBuilt for trust, not just volume.
Consent first
Contributors explicitly agree to data usage terms before participating.
Local context
Data reflects genuine cultural nuances and dialectal accuracy.
Verified quality
Rigorous multi-stage review ensures only high-fidelity data is delivered.
Fair work
Prompt, transparent payouts for accepted contributor tasks.
Tell us what language data you need.
Share your target languages, collection type, review requirements, and timeline. We'll help define the right collection plan or dataset access path.
Get in touch
Reach out directly to our team to discuss your dataset requirements.
Email hello@mahder.ai