Evaluate Clinical AI Tools Without Patient Data.
Your organization needs to evaluate AI tools and clinical technologies that promise to transform drug development, patient care, or operational efficiency, but vendors require data you cannot provide. Testing requires patient records, clinical trial data, or research protocols—all protected under HIPAA and other regulations. Procurement committees demand rigorous evaluation. You need to validate vendor claims and compare competing solutions, but you can't access the data required for meaningful testing.
- Objective Vendor Comparison: Benchmark solutions against fit-for-purpose datasets with ground truth labels to make data-driven purchasing decisions instead of relying on vendor claims
- Eliminate Delays: Launch vendor tool evaluations immediately without lengthy internal reviews or costly patient approval processes, reducing purchasing cycles from year(s) to weeks
- Prove ROI to Build Your Business Case: Rather than vendor-curated demos, test tools on complex, realistic clinical scenarios that reveal true capabilities, ensuring that they deliver actual value
Develop Healthcare AI That Actually Works, Without PHI.
Your team needs to build clinical AI tools, internal models, or healthcare analytics platforms, but development requires patient data you cannot access. Building predictive models for adverse event detection, patient risk stratification, or clinical decision support demands realistic healthcare data for training and testing.
You can't use actual patient records in development environments, and synthetic data generated from statistical patterns fails to capture the clinical complexity and edge cases your tools must handle in real-world deployment. Your development cycles extend indefinitely while waiting for data partnerships or approvals, and when tools finally launch, they underperform because they weren't tested against the reality of actual clinical practice.
- Faster Time to Market: Build and test healthcare AI tools immediately without lengthy internal approvals or compromising patient privacy, reducing development cycles
- Higher Quality Releases: Validate algorithms against complex clinical scenarios and edge cases before deployment, surfacing issues in development rather than in patient care settings
- Streamline Deployment with Anonymized Data: Eliminate expensive post-launch fixes and compliance violations by testing on realistic complexity during development, not after FDA submission or market release
Develop Clinical Expertise with Confidence.
Your organization needs to train clinical staff on new systems and optimize care delivery workflows, but you can't use patient data for training. Nurses need hands-on experience with EHR systems, research coordinators need to master clinical trial protocols, and care teams need to test new processes—yet providing realistic training without exposing PHI is impossible.
Your training remains theoretical, your workflows remain untested, and you lack the safe environment needed to develop expertise without HIPAA violations.
- Accelerated Onboarding: Train clinical staff and research coordinators on realistic patient scenarios, reducing time-to-competency from months to weeks
- Workflow Optimization Without Risk: Test and refine care delivery processes before deploying on real patients, identifying issues and inefficiencies in a HIPAA-compliant environment
- Build System Expertise: Give teams hands-on experience with EHR platforms, trial management systems, and clinical AI tools, building competence to maximize technology investments and improve patient outcomes
Our patent-pending process uses AI agent simulations to create realistic clinical trial participant data, adverse event reports, EHRs, physician-patient communications, regulatory submissions, and research documentation. All with embedded scenarios designed to optimize study design, test healthcare AI tools, and validate compliance systems.
Seedless' founding team led critical functions at global pharmaceutical companies, navigating HIPAA compliance, clinical trial data management, and AI deployment in heavily regulated environments. This healthcare domain expertise, combined with technical backgrounds in AI/ML simulations, uniquely positions them to address the data scarcity problems blocking healthcare AI adoption and clinical research optimization. This isn't a generic data company adapting to healthcare use cases, it's purpose-built technology from industry veterans who understand your challenges.
Evaluate Clinical AI Tools Without Patient Data.
Your organization needs to evaluate AI tools and clinical technologies that promise to transform drug development, patient care, or operational efficiency, but vendors require data you cannot provide. Testing requires patient records, clinical trial data, or research protocols—all protected under HIPAA and other regulations. Procurement committees demand rigorous evaluation. You need to validate vendor claims and compare competing solutions, but you can't access the data required for meaningful testing.
Our datasets deliver measurable impact on your evaluation process and technology decisions:
- Objective Vendor Comparison: Benchmark solutions against fit-for-purpose datasets with ground truth labels to make data-driven purchasing decisions instead of relying on vendor claims
- Eliminate Delays: Launch vendor tool evaluations immediately without lengthy internal reviews or costly patient approval processes, reducing purchasing cycles from year(s) to weeks
- Prove ROI to Build Your Business Case: Rather than vendor-curated demos, test tools on complex, realistic clinical scenarios that reveal true capabilities, ensuring that they deliver actual value
Develop Healthcare AI That Actually Works, Without PHI.
Your team needs to build clinical AI tools, internal models, or healthcare analytics platforms, but development requires patient data you cannot access. Building predictive models for adverse event detection, patient risk stratification, or clinical decision support demands realistic healthcare data for training and testing.
You can't use actual patient records in development environments, and synthetic data generated from statistical patterns fails to capture the clinical complexity and edge cases your tools must handle in real-world deployment. Your development cycles extend indefinitely while waiting for data partnerships or approvals, and when tools finally launch, they underperform because they weren't tested against the reality of actual clinical practice.
Our datasets deliver measurable impact on your development efficiency and product quality:
- Faster Time to Market: Build and test healthcare AI tools immediately without lengthy internal approvals or compromising patient privacy, reducing development cycles
- Higher Quality Releases: Validate algorithms against complex clinical scenarios and edge cases before deployment, surfacing issues in development rather than in patient care settings
- Streamline Deployment with Anonymized Data: Eliminate expensive post-launch fixes and compliance violations by testing on realistic complexity during development, not after FDA submission or market release
Develop Clinical Expertise with Confidence.
Your organization needs to train clinical staff on new systems and optimize care delivery workflows, but you can't use patient data for training. Nurses need hands-on experience with EHR systems, research coordinators need to master clinical trial protocols, and care teams need to test new processes—yet providing realistic training without exposing PHI is impossible.
Your training remains theoretical, your workflows remain untested, and you lack the safe environment needed to develop expertise without HIPAA violations.
Our datasets deliver measurable impact on your team's capabilities and operational efficiency:
- Accelerated Onboarding: Train clinical staff and research coordinators on realistic patient scenarios, reducing time-to-competency from months to weeks
- Workflow Optimization Without Risk: Test and refine care delivery processes before deploying on real patients, identifying issues and inefficiencies in a HIPAA-compliant environment
- Build System Expertise: Give teams hands-on experience with EHR platforms, trial management systems, and clinical AI tools, building competence to maximize technology investments and improve patient outcomes
