DataFramer

Sharing a quick demo video from the team at DataFramer: generating 1,000 synthetic EHR records with exact target distributions (and conditional rules), then validating and iterating. Super useful for dev/testing when real EHR access is limited. https://lnkd.in/emFxVemt

Same LLM, Different Results. We took Claude Sonnet 4.5, fed it 50K-token seeds from Wikisource, Gutenberg, Wiki Medical, and Real Estate datasets, and then watched raw prompting collapse into short, repetitive "summary essays". Yet DataFramer, with the same model, generated full-length, 50K-token outputs that matched the seed styles. Blind Gemini 3 Pro evals across 7 metrics (diversity, style matching, length, quality, artifacts, validity, overall) favored DataFramer in all datasets. Read more on our blog: https://lnkd.in/efp2z8Eh All data (seeds, Dataframer outputs, and baseline outputs) is also available on HuggingFace: https://lnkd.in/eBjhZXRp

Big update 🚀 DataFramer is now listed on the AWS Marketplace. Generate high-quality realistic data for Insurance, Healthcare, Finance, text-to-SQL, and other use cases. Now even easier to adopt! Check it out: https://lnkd.in/ejaiSUe3

See how our healthcare and insurance customers generate diverse, high-fidelity, pre-evaluated synthetic patient histories to overcome data access barriers, while preserving privacy - to accelerate research and improve model performance. Speaker: Puneet Anand is a Co-founder and CEO at DataFramer, and works with leading Healthcare, Life, and Medical insurance teams to generate EHR, patient histories, insurance submissions, fraud, and text2sql datasets. DataFramer is a Synthetic Data Generation power tool that gives you complete control over your dataset generation workflow, allowing you to evaluate datasets automatically and with human experts.

According to Brookings Institution, Healthcare AI projects struggle because of data access limitations and regulatory barriers. The best data is the hardest to use! Patient information is sensitive, highly regulated, and often locked inside siloed systems. As a result, teams face long approval cycles, limited access to clinical records, and datasets that are too small or biased to train reliable models. Synthetic data tools offer a practical path forward. They give domain experts the control to recreate clinical patterns and EHR/patient histories without exposing any individual’s details. This allows safer collaboration, broader testing, and more realistic model development. Want to know how? Webinar Live Demo on Dec 9th: https://lnkd.in/eZegiqYS YouTube Recorded Demo: https://lnkd.in/eekNu_t5

AI in insurance faces a paradox: It needs data to predict risk, but the best data is too sensitive to use. Life and medical insurers sit on years of claim history, demographic detail, and risk patterns. Yet, privacy laws and siloed systems make it nearly impossible to evaluate and train models responsibly. That’s where synthetic data comes in. It recreates real-world insurance patterns, without exposing any personal information. The result? Safer collaboration, faster model evaluations, training, and better fairness in underwriting and claims prediction. Because the future of AI in insurance isn’t about more data - it’s about ethical, usable data. Webinar Live Demo on Dec 9th: https://lnkd.in/eZegiqYS YouTube Recorded Demo: https://lnkd.in/eekNu_t5

Most AI models fail, or hallucinate - not because of bad algorithms, but because of missing examples. In finance, it might be an uncommon or new fraud pattern. In healthcare, a rare disease. In insurance, a rare claim type. The result: models that perform well in limited testing but stumble in production. One way researchers and data scientists address this is by using synthetic data, i.e. data generated to statistically resemble real-world data, esp. rich in rare scenarios. When done responsibly, it helps fill data gaps, test robustness, and reveal model weaknesses before deployment. The takeaway: 👉 Don’t just train your models on what’s common. 👉 Train them for what’s possible.

One of the biggest challenges in AI development today is ensuring privacy and compliance while still testing and training high-performing models on real-world data. Synthetic data offers a practical solution. It isn’t “fake” or "fabricated". It is artificially generated data that mimics real-world patterns, allowing teams to: - Emulate real-world behavior without exposing sensitive information - Ensure data privacy and regulatory compliance - Achieve specific data distributions for model training - Enable simulation, scaling, and anonymization use cases And the benefits are significant: - Reduced experimentation time - Lower AI project costs - Access to balanced, diverse data instantly - Compliance and privacy by design - Preservation of real-world characteristics Learn more: www.dataframer.ai Watch our demos: https://lnkd.in/eNrgFZGt