Data collected, created and labelled by verified people who match the users your model will serve, across 46+ languages and 130+ countries.
Background noise outside the brief. The clips are re-recorded by the same contributor.
The bias screen flags the gap, and more contributors are matched to fill it.
Agreement between reviewers drops on the missing type until it is collected.
Figures from published Oprimes programs: cybersecurity, automotive voice, ADAS, healthcare vision and gaming speech.
If the data misses the people, the model misses them too.
A facial recognition model trained on data without real-world diversity was biased across ethnicities, lighting and expressions, and unreliable on low-end devices. A mix of synthetic data, real-world collection and human validation fixed it.
An in-car voice system needed natural, non-robotic German from native speakers across multiple dialect zones. 150,000 recorded phrases came from verified speakers.
Automated speech engines mis-transcribed non-native English speakers in meeting audio. High-proficiency non-native linguists corrected it to 98% transcription accuracy.
Across text, voice and speech, image, video and documents.
Contributors matched by language, region and dialect, across 46+ languages.
Reference sets that your models are trained and judged against.
Text, voice, image, video and document data from verified contributors.
Labels applied by reviewers who understand the task and the domain.
Finance, healthcare, automotive and retail data built by people who know the field.
People create and review data so the model learns from real judgment.
Datasets that look like the people your model will serve.
Highest demand first. Each starts from the need it answers.
Improve LLM response quality, alignment, safety, and reasoning
Users compare two AI responses and select the better one; rate helpfulness, accuracy, safety, groundedness, and relevance; identify hallucinated responses.
Train and evaluate AI agents and copilots on real-world tasks
"Open a savings account," "File an insurance claim," "Book a doctor appointment," "Track my order": users assess whether the AI agent completed the task correctly and efficiently.
Improve speech recognition and voice assistants
Audio recordings across age groups, genders, regions, and accents; speech transcriptions; wake-word testing; pronunciation variants; noisy-environment recordings (car, street, home).
Ensure AI works naturally across languages and cultures
Translation quality reviews, culturally appropriate responses, local phrase validation, sentiment interpretation, regional language prompts and expected responses.
Measure trustworthiness and production readiness
Users verify whether AI-generated answers are factually correct, identify hallucinations, detect biased responses, validate citations, test edge cases and failure scenarios.
Train and validate vertical-specific AI models
Healthcare symptom-to-response validation, banking KYC conversations, loan eligibility assessments, insurance claim scenarios, vehicle troubleshooting conversations, product recommendation quality reviews.
The platform loop, applied to data creation.
Contributors matched by language, region, device, demographic and domain.
Schema, guidelines, volumes and consent requirements.
Tasks are routed to matched contributors through autonomous workflows.
Every submission is checked before it counts.
Model results guide the next collection round.
Data about people needs more care than data about things.
Collection is consent-based, and consent is recorded with the data.
Where personal data should be excluded, the work is designed around it. A hospital project identified staff using clothing and visual cues only, with no facial recognition data.
A Hindi voice dataset was delivered in two phases with no consent or data violations in either.
Use cases from the platform, by technology and industry.
Oprimes designs, runs and reports the program end to end.
Your own testers or panel, run on Oprimes workflows and quality controls.
Your team runs studies and evaluations on the platform.
The platform and crowd delivered under your brand.
Managed suits custom datasets. Bring-your-own-Crowd suits teams with their own contributors. SaaS suits ongoing annotation.
Text, voice and speech, image, video and document data, collected from verified contributors or annotated from your own material.
Contributors are matched by language, region and dialect, and reviewed by linguists. Programs cover 46+ languages.
Submissions are checked against gold-standard tasks, scored for agreement across contributors and screened for bias and consistency before they are accepted.
Yes. Collection is consent-based, and privacy-first options are available where personal data should be excluded.
It depends on volume, language and domain. Readymade datasets are built to give a validation sample in days, and custom collection is scoped for each program.
Yes. We annotate your data with domain-matched reviewers under NDA.
Share the languages, volumes and domain. We will scope a sample.
Better data starts with the right people.
Book a 30-minute consultation with an Oprimes AI Trust Specialist. We will map your use case, recommend the right service pillar, and give you a delivery timeline before you commit to anything.
Trusted by 80+ enterprise AI teams across 6 industries. No obligation on first consultation.