The Multimodal Data Platform for Dataset Engineering

Mixpeek indexes video, image, audio, and text data with semantic embeddings, enabling powerful search and filtering during dataset creation. You can find specific examples using natural language queries ('images of crowded streets at night'), identify similar samples for balanced datasets, and detect duplicates or near-duplicates automatically. This reduces manual curation time by 60-80%.

AI pre-tagging uses Mixpeek's extractors to automatically generate initial labels for images, videos, and audio before human annotation. Annotators review and correct AI suggestions rather than labeling from scratch. This increases annotation throughput by 3-5x, improves label consistency across annotators, and reduces labeling costs by 40-60%.

Yes, Mixpeek tracks complete dataset lineage including source data provenance, annotation history, version snapshots, and train/val/test splits. You can recreate any dataset version, compare versions for quality improvements, and audit which data was used for specific model training runs. This supports reproducibility requirements for production AI systems.

Mixpeek's semantic search understands concepts beyond keywords. You can search for rare scenarios like 'person partially occluded by shadow' or 'low-light outdoor audio with wind noise' and find matching examples even if those exact words don't appear in metadata. This dramatically improves edge case coverage for robust model training.

There is no Labelbox, Scale AI, V7, SuperAnnotate or Label Studio connector today, so nothing pushes pre-tags or pulls completed labels on its own. The API returns predictions as JSON with confidence scores, which is the input most annotation tools accept for pre-labelling, and finished annotations can be written back as document metadata. The glue between the two is yours to build.

Mixpeek uses perceptual hashing and semantic similarity to detect exact duplicates, near-duplicates, and semantically similar examples. This includes detecting crops, rotations, color adjustments, and compression variants of the same content. Deduplication typically reduces dataset size by 15-30% while maintaining diversity.

Yes, Mixpeek supports active learning workflows by identifying high-value examples for annotation. The system can find samples most different from existing training data, detect distribution shifts, and prioritize uncertain examples based on model predictions. This optimizes annotation budgets by focusing on data that improves model performance most.

Mixpeek includes quality validation tools that detect inconsistent labels, outlier annotations, and labeling errors. Inter-annotator agreement metrics identify problematic samples requiring expert review. Automated checks validate label schema compliance and catch common errors (missing required fields, invalid values) before data enters training pipelines.

Pricing is based on dataset size (number of samples), storage volume, and annotation throughput. Most teams see 3-5x ROI through: (1) 60% faster dataset development, (2) 40% lower annotation costs from AI pre-tagging, (3) reduced model failures from better quality data. We offer startup pricing for teams under 100K samples and enterprise pricing for multi-million sample datasets. Contact us for custom pricing.

Mixpeek handles datasets from thousands to hundreds of millions of samples using distributed infrastructure. Search queries return results in under 100ms even across 100M+ samples. Batch processing supports parallel ingestion, feature extraction, and annotation at 100K+ samples per hour. Storage automatically scales with dataset growth.