Frequently asked questions
Answers to the questions AI teams ask most about working with One6: process, quality, data rights, languages and experts.
Contributor Network
Can you staff specific languages or regions?
Yes. Contributors are recruited to match the languages, locales and demographics the project needs. Tell us your requirements and we'll confirm availability and contributor profiles during scoping.
What kinds of experts are available?
We source specialists in fields including engineering, mathematics, medicine, law, research, finance, statistics and physics. Tell us the domain and level of expertise required and we'll confirm the reviewer profile during scoping.
How do you make sure contributors are qualified?
Contributors pass a skills test or trial task before joining a project and are trained on its guidelines before production work. Their accuracy and output are tracked throughout, and anyone who falls below the quality bar is retrained or removed.
Are contributors bound by confidentiality?
Yes. Project materials, client information and instructions are confidential, and contributors may not share them outside the project. Projects can also run under NDA.
Project Workflow
How do you make sure the data matches our requirements?
Every program starts with a written specification covering format, metadata, contributor profile and acceptance criteria. We run a small pilot, review the output with you, and only scale once the pilot meets the agreed bar.
Do you work in our annotation tool or yours?
Either. We can work inside the platform your team already uses or propose tooling during scoping. The choice is confirmed before the pilot.
Who owns the data?
Data ownership and usage rights are defined in the project agreement before collection begins. Projects can run under NDA.
Delivery Methodology
How will we know how the project is going?
You receive regular progress updates by email or on scheduled calls throughout delivery.
What happens if a contributor's quality drops?
Each contributor's accuracy and output are tracked throughout the project. Contributors who fall below the quality bar are retrained or removed from the project.
How are the guidelines managed?
A written guidelines document is agreed with your team before work begins and kept up to date as questions are resolved during delivery.
Quality System
How is quality measured?
Quality checks are agreed upfront and typically combine gold-standard examples, reviewer sampling and agreement between annotators. You see the results during the pilot.
How do you keep ratings consistent across reviewers?
Reviewers are calibrated on shared examples against your rubric before production work, and agreement is monitored throughout the project.
Can you audit or re-label an existing dataset?
Yes. Re-labeling or auditing an existing dataset with quality problems is one of the projects our annotation service supports.
Trust & Security
Who owns the data you collect?
Data ownership and usage rights are defined in the project agreement before collection begins.
Can the project run under NDA?
Yes. Projects can run under NDA, and contributors are required to keep project materials and client information confidential.
Do speakers consent to their voice being used for AI?
Consent and usage terms for every recording are agreed with you during scoping and put in place with contributors before collection begins.
AI Training Data Collection
How do you make sure collected data matches our requirements?
Every program starts with a written specification covering format, metadata, contributor profile and acceptance criteria. We run a small pilot, review the output with you, and only scale once the pilot meets the agreed bar.
Can you collect data in specific languages or regions?
Yes. Contributors are recruited to match the languages, locales and demographics the project needs. See our multilingual data service for more on language coverage.
Who owns the collected data?
Data ownership and usage rights are defined in the project agreement before collection begins. Projects can run under NDA.
Data Annotation & Labeling
How is annotation quality measured?
Quality checks are agreed upfront and typically combine gold-standard examples, reviewer sampling and agreement between annotators. You see the results during the pilot.
Can you handle specialized domains?
Yes. For technical material we staff domain experts rather than general annotators — see expert human evaluation.
Multilingual AI Data
Which languages do you support?
Coverage depends on the project's language and dialect requirements. Tell us the languages you need and we'll confirm availability and contributor profiles during scoping.
Are contributors native speakers?
Yes — language work is staffed with native or near-native speakers of the target language and, where relevant, the specific regional variety.
Speech & Voice Data
What audio formats and quality can you deliver?
Sample rate, format, channel setup, noise conditions and metadata are defined in the project spec, and recordings are checked against it before delivery.
Have a data project in mind?
Tell us what your model needs. We'll come back with questions, a proposed approach and a pilot plan.