Calibration

  • Fine tune custom embedding vectorizer models a labeled dataset for higher accuracy vectors (optional). Embeddings uncertainty is available for custom vectorizers.

  • Train Redundancy filters or (use our defaults) to clean your archive during indexing (optional) (If your data is video, sound or text you can use our automated similarity dataset extraction)

  • Train Relevance filters to clean your archive during indexing (optional)

  • Train Embedding Vector Hashing models to enable orders of magnitude faster search and clustering in large archives and datasets. (optional)

Embeddings Vectorization Indexing

  • Multi-modal support: Image, Video, Sound, Text, 3D model/Point Cloud

  • Use pre-trained or custom vectorizer models

  • Vectors can also be hashed for operational speed up

  • We store your vectors and hashes in the cloud

  • No content/data files are kept after vectorization, safeguarding your data privacy

  • You can send vectorized embeddings for data privacy

  • You can retrieve the vectorized embeddings to use on your workflows

Analysis

  • Proprietary pipelines of vector similarity clustering and sorting routines crunch your indexed data to:

  • Sort, cluster and prioritize data reviewing options

  • Quantitative data distribution metrics to compare your datasets/archives or compare againts benchmarks

  • Take score data from experiments with your content and predict the optimal content for any task

  • Find non-obvious content connections in your archives with cross referencing

  • Pca dimensionality reduction for graphical exploration insights

  • Balancing recommendations through prioritized deletion and sourcing

  • Quantify originality

  • Reach a consensus that accurately reflects the collective input while maintaining relevance and fairness.

  • Tracing of how and where specific preferences originated and evolved.

Exploration

  • Enable fast, affordable expert human review by focusing on high impact data with high productivity graphical browsing tools:

  • Overview and understand your data with highlights, clusters, inliers, outliers and more

  • Rate items in an archive with preference weights to customize the inlier/outlier browsing to your taste.

  • Use reverse search to validate the presence of essential requirements and detect infringing material

  • Cross reference multiple browsing criteria to discover complex patterns, anomalies, nuances

Enable fast, affordable multi-modal GPT AI review by spending api calls only on the high impact data.

Datasets/Model Outputs benefits

Archives / Data lakes benefits

Auto highlight, auto trim or auto segment/crop your data files

Automate efficient content highlighing trimming, segmentation/cropping.

Remove redundancy and over-representation for data balancing and training efficiency. Prevent Model Collapse from accidental training on AI model outputs

Automate efficient content ingestion filtering redundancy and irrelevance

Remove IP protected/copyrighted material from datasets, quantify originality

Filter copyright infringing content during ingestion, quantify originality

Overview quickly with clusters, highlights, inliers, outliers and pca dimensionality reduction for graphical viewing

Overview quickly with clusters, highlights, inliers, outliers and pca dimensionality reduction for graphical viewing. Enable third parties to browse your archives

Validate the presence of essential content and detect infringing content with reverse search. Search with custom examples or use our compiled common problems example archives (pornography, weapons, violence, discrimination etc).

Validate the presence of essential content and detect infringing content with reverse search. Enable third parties to search your archives. Search with custom examples or use our compiled common problems example archives (pornography, weapons, violence, discrimination etc)

Translate embedding vectors to search an archive of a data type with examples from another data type

Provide a score for multiple ids and we will find the theoretical optimal/retrieve the closest to optimal content, style, prompting stategy etc.

Analyse distribution, diversity and bias metrics to make data driven comparisons between your datasets/archives or to compare your data with external benchmarks. Find non-obvious content connections in your archives with cross referencing

Balance your dataset with vector similarity clustering and sorting based recommendations for balancing data through deletion of over represented and additional sourcing of under represented data

Improve your recommendation engines with vector similarity clustering and enhance your existing colaborative filters

Guide your archive maintenance and expansion efforts

Unlock hidden Knowledge connections between the contents in different indexed documents by providing the referencing between your documents.

Enable source attribution for generative models trained on your indexed data

Archives can be configured with read-write privileges to control access, enabling safe collaborative indexing