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.

Calibrate an archive for relevance and use it to validate your synthetic data production pipelines.

Use smart prioritization, sampling/trimming/image segmentation/cropping to increase the productivity and accuracy of reviewing efforts.

Use smart prioritization, sampling/trimming/image segmentation//cropping 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.

Facilitating faster and more cost-effective data transfer for brokerage and internal sharing thanks to the efficient vectorized format.

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, especially in high subjectivity archives.

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, image segmentation/cropping.

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.

Using our score based optimization you can predict the ideal content for any task based on experimental data.

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

Convert an archive or data lake into usable datasets for internal training or licensing.

Smaller, more manageable datasets are faster and cheaper to transfer for licensing or internal distribution.

Check our Guides for detailed use cases and workflow implementations. Contact us at hello@data2vector.ai for trials and API keys