An Innovative Data DNA Approach

Ascend introduces an inovative data DNA technique, enabling the extraction of data insights without storing the actual data. This approach can replicate high-quality, hallucination-free synthetic data at scale, accurately reflecting the properties of original production data while ensuring compliance with legal, privacy, security, governance and data quality standards.

Use Cases

Remote Work w/o Sharing Production Data
Cost cutting by enabling secure remote work through unique Data DNA and generating data at scale.
Data Profiling / Curation
Analyze data quality and patterns while optimizing and standardizing data for AI/ML model reliability.
Model Training / Tuning
Optimize and train ML models using privacy-compliant synthetic data that preserves statistical properties.
Product Development
Use synthetic data for application testing and development in non-production environments.
Stress Testing
Generate synthetic data at scale for thorough product stress testing and validation.
Low Environment R&D
Enable rapid R&D using high-quality synthetic data for new innovations without regulations risk.

Targeted Verticals

Healthcare
Clinical trial data, PHI data, patient demographics data, etc.
Finance
Credit card data, transaction records, etc.
Insurance
Claims data, policyholder data, etc.
Tech Companies
Customer PII data for app development and lower environment R&D.
Retail/E-Commerce
User behavior data, customer purchase histories, etc.
Environmental Science
Climate data, pollution data, etc.
Pharmaceuticals
Drug trial data, patient data, etc.
Telecom
Network traffic data, call record data, location data, etc.

Deploy on Cloud or On-Prem

Easy Integration: Empower seamless automation via REST APIs, fostering effortless integration with third-party tools within our ecosystem.
Flexible Deployment: This solution seamlessly adapts to various environments, from public and private clouds to hybrid setups, and it is even deployable on-premise for maximum flexibility.
Highly Performant: Enhanced efficiency through multi-threaded processing, parallel dataset handling, and expedited export/download options for multiple files.