AI & Data

High-quality, safe data for generative AI

Build enterprise-grade AI with high-quality, safe synthetic data to train, fine-tune, and evaluate model performance.

Safe synthetic data to train, fine-tune, and evaluate AI models

  • Generate high-quality datasets for AI/ML development

  • Design safe versions of your data

    Break silos and make data accessible across your organization safely and without compromising on privacy.

  • Improve model performance

    A model is only as good as the data it’s trained on—use synthetic data to improve model performance with domain-specific, quality data on-demand.

  • Generate data from scratch

    Solve the “cold-start” problem and create high-quality datasets such as text-to-code examples for popular programming languages like SQL and Python.

The AI & Data challenge

The data bottleneck

With the emergence of generative AI, training and fine-tuning these powerful models requires a substantial amount of high-quality training data. Gathering and collecting this domain-specific data is a common challenge for many organizations.

Stalled innovation

Taking AI from concept to production demands vast data for training, yet teams often hit what's known as the data wall. This wall refers to obstacles obtaining, processing, and maintaining data in a way that is sustainable, cost-effective, and privacy-compliant.

Sensitive, private customer data

Protecting sensitive customer data is a priority for AI & Data companies, with global regulations like GDPR and CCPA imposing heavy penalties for non-compliance. As AI adoption grows, the need for safe, privacy-preserving data is essential.

Inaccessible, unavailable data

Many companies lack sufficient data for AI, while others have data that’s unusable due to privacy restrictions. Traditional data collection, labeling, and enrichment methods are costly, time-consuming, and error-prone.

The Gretel solution

Unlock the value of your data

Synthetic data empowers AI and data-driven companies to train, test, and evaluate a variety of models, including foundation models, large language models (LLMs), and small language models (SLMs). With Gretel, you no longer have to choose between data utility and privacy—our platform unlocks the value of your data while upholding stringent privacy and quality standards.

  • Key benefits

    Build data-centric AI with high-quality, safe synthetic data

  • Protect sensitive data

    Synthetic data lacks the personal identifiers of real-world data but matches the utility of real customer data, making it a reliable and safe alternative for training models at scale.

  • Accelerate AI/ML innovation

    Take AI from idea to production with high-quality, safe synthetic data purpose-built for training, fine-tuning, and evaluating language models. Improve model performance with synthetic data that is unique to your business without compromising on data privacy. 

  • Reduce operational overhead

    Traditional processes for labeling and structuring data can be time-consuming and costly. With synthetic data, businesses can streamline AI development processes with high-quality, domain-specific synthetic data in minutes and at a fraction of the cost.

At a glance

AI & Data companies see real-world results

150K+

Developers using Gretel Cloud.

62%

Improvement in a text-to-code use case for a commercial state-of-the-art model.

35%

Improvement in task-specific correctness.

Key use cases

Better data for AI & Data companies building generative AI

From solving the cold-start problem to creating safe versions of private customer data, Gretel helps AI & Data companies build better models faster.

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Training or fine-tuning natural language models including LLMs and SLMs
Improve model performance on tasks like text-to-code with synthetic SQL or Python datasets
Testing and evaluating machine learning (ML) models
Augmenting data for ML training
Evaluating retrieval-augmented generation (RAG) models
Create safe versions of private customer data

AI & Data Resources

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