The data engine for world‑class vision language models
Source, vet, and manage expert annotators in one place to accelerate precise video annotations.
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Case study
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Luma Labs partners with micro1 to onboard 100s of video annotators and power the next generation of their world-class video language model
Power your AVLMs with expert sourced, deeply vetted, and precisely annotated video data
Our core capabilities
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Scale & Quality
Fully managed by expert annotators, our engine delivers consistent, high‑precision labels at every stage of your video data pipeline
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Modality‑Focused
Built specifically for video: frame‑level, temporal, natural‑language annotations designed for VLM training.
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Flexible & Cost‑Effective
We batch, stitch, or chunk videos based on complexity, optimizing cost and throughput
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Data Diversity
Support for full‑motion, multi‑scenario, and edge‑case video coverage—including multilingual contexts and visual‑verbal alignment.
How it works
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We use our AI recruiter to source and vet expert video annotators at scale.
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You send us video inputs, such as S3 buckets, youtube, mp4, or raw uploads.
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We assign top experts to begin structured annotation using our data platform.
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The experts' performance is managed directly within our platform.
Use Cases
Empower your VLMs to master real-world video understanding with expert-labeled data across diverse, high-impact tasks.
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Captioning & Transcription
Detailed descriptions of actions, environments, and dialogue.
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Multimodal QA
Segment‑aware QA labels aligned with video segments.
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Behavioral Scene Understanding
Annotate speaker intent, emotion, interactions—ground truth context for intelligent VLM behavior.
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Our experts
O.K.
Perception Annotation Engineer
Exp: 5+ years
Rwanda
Vetted skills:
Attention to Detail
Data Annotation
Quality Assurance
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F.S.
Data Annotator
Exp: 7+ years
Kenya
Vetted skills:
Data Annotation
Quality Assurance and Validation
Handling Ambiguity
+1
H.O.
People Data Analyst
Exp: 5.8+ years
Nigeria
Vetted skills:
Data Annotation
Quality Assurance and Validation
Handling Ambiguity
+1
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