At Krypton Labs AI, we empower businesses in finance, credit unions, healthcare, and e-commerce to generate high-quality, privacy-preserving synthetic data tailored to their unique needs. Our solutions help you accelerate AI adoption, overcome data bottlenecks, and ensure regulatory compliance—without compromising accuracy or security.
WHY SYNTHETIC DATA
Collecting real-world data is often expensive, time-consuming, or restricted.
Synthetic data enables innovation while adhering to HIPAA, GDPR, and financial regulations.
Train models on large, balanced datasets that reflect complex real-world distributions.
Generate unlimited data variations to power testing, validation, and simulations.
At Krypton Labs AI, synthetic data generation is not "one-size-fits-all." We deliver custom models, trained and validated for your industry and use case.
GANs (Generative Adversarial Networks)
For realistic image, text, and transaction data.
VAEs (Variational Autoencoders)
For structured data synthesis and dimensionality reduction
Diffusion Models
Cutting-edge generative models for high-fidelity, diverse synthetic data.
LLM-based Generators
Text and tabular data generation using domain-tuned large language models.
Healthcare : Patient records, imaging, diagnostics
Finance : Transactions, risk profiles, fraud detection
Credit Unions : Member transaction histories, lending
data, fraud simulation
E-commerce : Customer journeys, product catalogs,
personalization
Healthcare : Patient records, imaging, diagnostics
Finance : Transactions, risk profiles, fraud detection
Credit Unions : Member transaction histories, lending
data, fraud simulation
E-commerce : Customer journeys, product catalogs,
personalization
Statistical alignment with original datasets
Privacy leakage prevention
Downstream ML performance validation
Statistical alignment with original datasets
Privacy leakage prevention
Downstream ML performance validation
GANs (Generative Adversarial Networks)
For realistic image, text, and transaction data.
VAEs (Variational Autoencoders)
For structured data synthesis and dimensionality reduction
Diffusion Models
Cutting-edge generative models for high-fidelity, diverse synthetic data.
LLM-based Generators
Text and tabular data generation using domain-tuned large language models.
KEY FEATURES
Fully synthetic datasets eliminate re-identification risks.
Captures distributions, correlations, and rare events.
Built to align with GDPR, HIPAA, PCI-DSS and other standards.
Generate millions of records or images on demand.
Tailored to your domain and business problem.
We ensure synthetic data is trustworthy and useful through a multi-stage evaluation process
Comprehensive evaluation ensuring data quality, privacy, and utility at every step
KL-divergence, Wasserstein distance, and correlation tests.
Membership inference and attribute disclosure risk checks.
Benchmark ML performance on real vs. synthetic datasets.
Business rules and domain knowledge cross-check.
INDUSTRIES WE SERVE
Fraud detection training Synthetic transaction data for compliance testing Stress-testing risk models
Patient data anonymization Medical research datasets Drug discovery simulations
Customer behavior modeling Recommendation system training A/B testing datasets
Credit scoring models Member data protection Regulatory compliance testing
ENGAGEMENT MODEL
We follow a collaborative, transparent workflow
ENGAGEMENT MODEL
We follow a collaborative, transparent workflow
Understand Your Goals And Data Constraints
1
Build A Pilot Synthetic Dataset And Demonstrate Value
2
Deliver Production-Ready Synthetic Data Pipelines
3
Continuous Monitoring, Retraining, And Updates
4
Example Use Cases
Training fraud detection systems in banking with synthetic credit card transactions.
Real-time fraud detection
99.8% accuracy rate
Reduced false positives
Simulating rare disease patient records to improve diagnostic AI in healthcare
HIPAA compliant
Rare disease modeling
Clinical trial acceleration
Generating synthetic member transaction logs for credit unions to detect anomalies.
Member privacy protection
Risk modeling
Anomaly detection
Creating synthetic shopping journeys for personalization engines in retail.
Customer journey mapping
Personalized recommendations
Conversion optimization
We're eager to help! Reach out anytime and get the answers you need to move forward confidently.
Yes. Our synthetic datasets are statistically aligned with real-world data, validated for accuracy, and proven to work in downstream machine learning tasks.
We implement privacy audits (membership inference, attribute disclosure testing) to guarantee zero re-identification risks. All data complies with GDPR, HIPAA, and PCI-DSS standards.
Absolutely. By generating balanced datasets, synthetic data helps AI systems learn patterns of rare events without requiring vast real-world samples.
Finance, credit unions, healthcare, and e-commerce are leading adopters. However, any data-driven organization facing data scarcity, privacy concerns, or compliance challenges can benefit.
Timelines depend on complexity, but a pilot dataset can typically be delivered within 2–4 weeks after initial consultation.
Let Krypton Labs AI help you generate synthetic datasets that are secure, scalable, and tailored to your business.
Let Krypton Labs AI help you generate synthetic datasets that are secure, scalable, and tailored to your business.