Co-founder & CEO
Since Aug 2025 · 1 yr 3 mos
Profile summary: AstraZeneca Head of Legal Services
Seedless
Seedless generates privacy-compliant synthetic data, including emails, contracts, health records, and financial documents, so companies can test and train AI without exposing real customer information. Its agent-based role-playing and multi-LLM process produces realistic business scenarios with embedded edge cases and "answer keys" for objective benchmarking. The service serves enterprises in finance, life sciences, healthcare, and legal, including legaltech vendors and law firms.
- Artificial Intelligence
- Software Only
- Data & Analytics
- Enterprise Software
Updated 22 days ago
Funding
Funding not disclosed
Funding rounds are not available yet.
Founders
Product
Problem
Companies struggle to develop and validate AI models because high-quality data that complies with privacy rules is scarce. Regulations and security concerns prevent organizations from using sensitive proprietary information for testing and training, which slows AI adoption and reduces trust in AI-driven solutions.
Solution
Seedless provides a platform for generating realistic, privacy-compliant synthetic data to support AI model development and validation. Its proprietary process uses agent-based role-playing and world-building, combined with a multi-LLM orchestration framework, to create diverse content that reflects real-world business scenarios. The datasets include nuanced edge cases and exceptions that matter when evaluating AI accuracy and reliability. Because the data is not seeded with an organization's actual corporate data, AI tools can be tested and trained without exposing real people or privileged information. Each dataset is built to specification and includes embedded answer keys, or ground-truth annotations, so performance can be benchmarked against a known standard. These benchmarks help organizations build confidence in their AI deployments.
Target Audience
Primary customers are enterprises in finance, life sciences, healthcare, and legal sectors, including legaltech vendors, law firms, and corporate legal departments, that need privacy-preserving datasets for AI development, testing, and validation.
Features
- Synthetic data generation using agent-based role-playing and world-building across communications, documents, records, and reports
- Patent-pending process in which multiple LLMs operate in concert to produce statistically valid and realistic data
- Embedded "answer keys" (ground-truth annotations) for performance benchmarking and AI training validation
- Sector-specific datasets for finance (fraud detection, AML, regulatory stress testing), life sciences and healthcare (patient health data, clinical trials), and legal (relevance identification, fact-finding, contract management)
- Generation of emails, chat messages, contracts, reports, patient health information, financial records, and meeting transcripts