Multi-Modal Data Discovery & Preparation AI Cloud Services
Democratizing the Prioritization, Validation, & Refinement of Data at Scale
What we do
Our Managed AI Cloud Services for High-Precision Multi-Modal Data Discovery and Management combines chains of modular API calls with full anonymized data support to:
Review, Validate and Filter Data:
Enhance training efficiency, accuracy, and reduce bias by validating datasets, filtering data quality, and balancing data distribution, unlocking new business opportunities while promoting safe, ethical and explainable AI.
Automate Content Ingestion:
Boost productivity in data labeling and quality control by automating filtered content ingestion, transforming archives into valuable datasets.
Enhance Discoverability:
Improve search and recommendation capabilities to make archives more discoverable.
Proprietary AI Powered Similarity Indexing Engine
Converts Images, Videos, Sounds, Texts, 3D Models/Point Clouds into high accuracy & granularity Embedding Vector and Hash representations, automatically picking the highlights in arbitrarily long inputs.
AI based search and clustering bypasses metadata labeling costs, multi-source metadata/taxonomy incompatibilities, accuracy limitations and avoids the hallucination dangers of generative models.
The vectorizer models can be fine tuned on the client’s data.
You can fine tune custom data format translation models with your data. With a custom data translator you can search an archive of a data type with content from another data type.
Platform and Tools
Vectorized/embedded data archive hosting + API + Open sourced graphical tool for job placement and results viewing for project based clients.
Althought we do not store your data files (we only keep your embedding vectors and hashes) we can also provide vectorization and fine tuning scripts and pre-trained models allowing clients to vectorize on their end if required for data protection.
Training Datasets
Model Outputs
Data accuracy and distribution balance are key to improving model accuracy, training efficiency/speed and reducing bias.
Accuracy improvements can disproportionally unlock new business opportunities.
Dataset/Model Output transparency and bias reduction through high productivity review are crucial for safe and ethical ai deployment.
AI Training, Data Labeling, Data Brokerage
Improve the productivity and accuracy of data distribution/quality control processes for dataset validation.
Use smart prioritization, sampling/Trimming to increase the productivity and accuracy of reviewing efforts.
Use smart prioritization, sampling/Trimming and propagation to increase the productivity and accuracy of labeling efforts.
Balance datasets over and under represented assets to improve model accuracy, training efficiency and reduce bias.
Sort your data by quality and complexity to create a training curriculum with the best results .
Increase the productivity and accuracy of model output monitoring and evaluation processes.
Archives
Data Lakes
Content redundancy and irrelevance can lead to poor user experience, increased costs.
Content discoverability and relevance are key to improving user engagement, satisfaction and retention.
Relevance improvements can disproportionally unlock new business opportunities.
Companies Unlocking Value in their Media Archives / Data Lakes
Automate content pre-processing with automatic sampling, trimming.
Automate onboarding filtering with fine tunable relevance and redundancy filters.
Increase the discoverability of content through AI reverse search, clustering, highlighting.
Using our reverse search or clustering you can retrieve the relevant context for your AI prompts, staying within the model’s token window size.
Convert an archive or data lake into usable datasets for internal training or licensing.
Check our Guides for detailed use cases and workflow implementations. Contact us at hello@data2vector.ai for trials and API keys
Calibration
Fine tune custom embedding vectorizer models a labeled dataset for higher accuracy vectors (optional)
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
Pca dimensionality reduction for graphical exploration insights
Balancing recommendations through prioritized deletion and sourcing
Quantify originality
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 or auto trim your data files
Automate efficient content highlighing and trimming
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
Analyse distribution, diversity and bias metrics with “health reports” to make data driven comparisons between your datasets/archives or to compare your data with external benchmarks
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
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
API Integration
Click here for the api documentation
Click here for a guide on how to integrate the api with your database
Send us an email to set up your account.
Your account can host multiple archives, each dedicated to a single content type of data (Image, Video, Sound, Text, 3D Model/Point Cloud)
Your cloud account will host only the embedding vectors, the content files are deleted after the vector is indexed.
You can index unlimited contents in an archive.
Pricing
Monthly/Annual hosting cost tiered per indexed count
Charge per compute time priced as background or real time jobs
Consulting services to setup trials, processes, api integrations and data pipelines
Background jobs run in background instances at low rates:
Model fine tuning
Filter calibration
Data sub sampling
Data indexing
Real time jobs run in always on instances at higher rates:
Search
Clustering
Inliers and Outliers
Distribution Analysis
Click here for instalation and usage instructions
The Client instalable GUI is a No Code method of running all the api jobs.
You can access the open sourced code and install it in your machine.
The GUI tool maintains the indexed data and results in your hard drives and manages the api calls and file uploading for you.
You can keep an unlimited number of projects.
Your search, clustering and other results are cached and progressively refined as you add and remove data through redundancy/relevance filtering and balancing recommendations.
Authenticate with your account
Create or select a project
Populate your project directories with the data for the tasks you want to perform
Save time running a fully automated pipeline for common calibration, indexing and analysis use cases
Run API jobs individually
Track the progress and status of the jobs in the queue
Browse your data, switching between browsing criteria and data balancing recommendations criteria
Analyse the data distribution metrics for a “Health Report”
Take action like deleting or adding more data to balance your dataset and repeat the indexing and analysis steps
Free Trial (No Integration)
You can participate in a trial to test the accuracy and usefulness of our indexing technology for your usecase.
The purpose of a trial is to explore the results of vectorizing a client's data and running filters, searches, clustering, distribution analysis on it.
The trial requires a zip folder to be sent with the appropriate sub folders depending on the steps that will be included.
The process can include calibration/fine-tuning steps or simply use our pretrained models.
The calibration is recommended for accurate results.
The trial is also conducted to determine the labeled datasets' size required for good fine-tuning results.
Send us an email requesting a trial so we can set up an FTP user for you and send you the trial credentials and non-disclosure agreement.
Contact Us
Please reach out to us at hello@data2vector.ai