Metabit turns raw data into precise, consistent training data across text, images, audio, and video. Trained human experts do the labeling, AI handles the busywork, and every label is reviewed before it reaches your model.
Automation can label fast, but it can't tell you when it's confidently wrong. Ambiguous cases, edge behavior, nuance, and shifting guidelines are exactly where models fail, and where only trained human judgment holds up. Metabit puts experts at the center: AI pre-labels the routine, people resolve the hard cases, and every label is reviewed, measured, and traceable before it reaches your training set.
Every modality, handled by trained annotators and domain experts. We also take on specialized fields that need real subject-matter knowledge, including medical, legal, and financial data. AI accelerates the volume; experts own the accuracy.
Turn documents, messages, and transcripts into clean, labeled training data for language and NLP models.
Annotate images and video so computer-vision models can recognize and locate what matters.
Transcribe, tag, and structure speech, audio, and sensor data into training-ready labels.
Human preference, prompt evaluation, and strict safety data to align language models. We build the structured guidelines that keep your AI on track.
One accountable path. Each step is run by experts, by AI, or by both, and every label carries the record of who did what.
We agree on the goal and quality bar with your team.
ExpertsAI drafts labels to take out the repetitive work.
AIExperts handle the cases judgment can't be automated for.
ExpertsMultiple reviewers resolve disagreements into gold labels.
ExpertsLabels ship in your format, ready to train on.
PlatformAccountability and technical rigor are essential for AI teams. We don't farm out your proprietary data to anonymous crowds. Our workforce consists of Data Curation Specialists who understand ML workflows, complex schemas, and human-in-the-loop pipelines.
When you partner with Metabit, you get a dedicated squad. They learn your edge cases, adapt to your schema requirements, and become a direct extension of your internal ML engineering team.
The same metrics our annotators are accountable for, reported per project, not averaged into a brochure. Representative figures below.
AI can pre-label at scale. People make sure it's correct and consistent across thousands of edge cases. We hire experts who treat label quality as the product.
Help build and maintain high-quality datasets used in AI, machine learning, and scientific knowledge systems. This role combines data acquisition, quality assurance, metadata generation, and human-in-the-loop workflows.
Monitor dataset acquisition workflows, investigate and resolve API/download failures, inspect datasets for completeness, and generate structured metadata. You will critically review and edit AI-generated annotations to ensure strict accuracy across large collections.
Bring us your data and a labeling goal, or just the problem, and get back precise, consistent training data. Early access is open for AI and research teams.