The trusted benchmark for synthetic data quality — with zero data access

Validate synthetic data with complete confidence. SYNDATA analyses accuracy, privacy and utility — all without accessing your original datasets.

CUBIG verifies synthetic data accuracy and structure without accessing original data, using similarity checks and reliability scoring.

Validate synthetic data securely — without exposure

Validate with CUBIG’s Data Non-Access Technology. Your original data is never exposed, copied or transferred.

Zero original data access

Validate with CUBIG’s Data Non-Access Technology. Your original data is never exposed, copied or transferred.

CUBIG validates synthetic data accuracy without accessing any personally identifiable or sensitive original data, ensuring complete privacy.

Zero original data access

Validate with CUBIG’s Data Non-Access Technology. Your original data is never exposed, copied or transferred.

Zero original data access

Validate with CUBIG’s Data Non-Access Technology. Your original data is never exposed, copied or transferred.

Certified by Korea’s Personal Information Protection Commission (PIPC)

SYNDATA is the only institution in Korea authorised to validate synthetic data compliance under PIPC guidelines. Independent, verified and compliant by design.

Validation reports delivered within one working day

within a

1-day

Validation report

Receive a detailed validation report in as little as one hour. Reports include structure, similarity and compliance scoring for complete transparency.

Validation report

Receive a detailed validation report in as little as one hour. Reports include structure, similarity and compliance scoring for complete transparency.

Validation report

Receive a detailed validation report in as little as one hour. Reports include structure, similarity and compliance scoring for complete transparency.

Zero original data access

Validate with CUBIG’s Data Non-Access Technology. Your original data is never exposed, copied or transferred.

Certified by Korea’s Personal Information Protection Commission (PIPC)

SYNDATA is the only institution in Korea authorised to validate synthetic data compliance under PIPC guidelines. Independent, verified and compliant by design.

Validation reports delivered within one working day

within a

1-day

Validation report

Receive a detailed validation report in as little as one hour. Reports include structure, similarity and compliance scoring for complete transparency.

Zero original data access

Validate with CUBIG’s Data Non-Access Technology. Your original data is never exposed, copied or transferred.

Certified by Korea’s Personal Information Protection Commission (PIPC)

SYNDATA is the only institution in Korea authorised to validate synthetic data compliance under PIPC guidelines. Independent, verified and compliant by design.

Validation reports delivered within one working day

within a

1-day

Validation report

Receive a detailed validation report in as little as one hour. Reports include structure, similarity and compliance scoring for complete transparency.

Comprehensive validation reports for synthetic data

01.

Downstream Performance

Measure how synthetic data performs in real-world AI applications. Downstream tests confirm accuracy, reliability and model compatibility.

Validation results across key metrics-accuracy, recall, precision, F1 score, and confidence-confirm the synthetic dataset’s readiness for real-world AI use.

Validation covers key metrics — accuracy, recall, precision, F1 score and confidence — to confirm AI readiness and reliability.

#1.

Accuracy — Overall correctness of predictions

#2.

Recall — Ability to capture true positives

#3.

Precision — Reliability of positive predictions

#4.

F1 Score — Balance between precision and recall

#5.

Confidence — Certainty in model predictions

01.

Downstream Performance

Measure your data’s real-world impact on AI performance through key downstream metrics.

Validation covers key metrics — accuracy, recall, precision, F1 score and confidence — to confirm AI readiness and reliability.

#1.

Accuracy — Overall correctness of predictions

#2.

Recall — Ability to capture true positives

#3.

Precision — Reliability of positive predictions

#4.

F1 Score — Balance between precision and recall

#5.

Confidence — Certainty in model predictions

01.

Downstream Performance

Measure how synthetic data performs in real-world AI applications. Downstream tests confirm accuracy, reliability and model compatibility.

Validation covers key metrics — accuracy, recall, precision, F1 score and confidence — to confirm AI readiness and reliability.

#1.

Accuracy — Overall correctness of predictions

#2.

Recall — Ability to capture true positives

#3.

Precision — Reliability of positive predictions

#4.

F1 Score — Balance between precision and recall

#5.

Confidence — Certainty in model predictions

01.

Downstream Performance

Measure your data’s real-world impact on AI performance through key downstream metrics.

Validation covers key metrics — accuracy, recall, precision, F1 score and confidence — to confirm AI readiness and reliability.

#1.

Accuracy — Overall correctness of predictions

#2.

Recall — Ability to capture true positives

#3.

Precision — Reliability of positive predictions

#4.

F1 Score — Balance between precision and recall

#5.

Confidence — Certainty in model predictions

01.

Downstream Performance

Measure how synthetic data performs in real-world AI applications. Downstream tests confirm accuracy, reliability and model compatibility.

Validation covers key metrics — accuracy, recall, precision, F1 score and confidence — to confirm AI readiness and reliability.

#1.

Accuracy — Overall correctness of predictions

#2.

Recall — Ability to capture true positives

#3.

Precision — Reliability of positive predictions

#4.

F1 Score — Balance between precision and recall

#5.

Confidence — Certainty in model predictions

02.

Privacy Performance

Assess whether synthetic data maintains structure and perceptual consistency. Validate the data’s integrity and human-recognisable patterns through privacy metrics.

Structure integrity and perceptual consistency metrics confirm the synthetic data retains its original structure and human-recognizable patterns, with both results safely within the high-performance range.

Structure and perceptual consistency metrics confirm that synthetic data retains schema, relationships and natural variation — all within the high-performance range.

#1.

Structure — Maintains format, schema and relationships

#2.

Perceptual — Preserves human-recognisable patterns and distributions

02.

Privacy Performance

Synthetic data that looks, feels, and performs like the real. Mirrors the performance, distribution, and patterns of your data.

Structure and perceptual consistency metrics confirm that synthetic data retains schema, relationships and natural variation — all within the high-performance range.

#1.

Structure: Maintains data format, schema, and relationships

#2.

Perceptual — Preserves human-recognisable patterns and distributions

02.

Privacy Performance

Assess whether synthetic data maintains structure and perceptual consistency. Validate the data’s integrity and human-recognisable patterns through privacy metrics.

Structure and perceptual consistency metrics confirm that synthetic data retains schema, relationships and natural variation — all within the high-performance range.

#1.

Structure — Maintains format, schema and relationships

#2.

Perceptual — Preserves human-recognisable patterns and distributions

02.

Privacy Performance

Synthetic data that looks, feels, and performs like the real. Mirrors the performance, distribution, and patterns of your data.

Structure and perceptual consistency metrics confirm that synthetic data retains schema, relationships and natural variation — all within the high-performance range.

#1.

Structure — Maintains format, schema and relationships

#2.

Perceptual — Preserves human-recognisable patterns and distributions

02.

Privacy Performance

Assess whether synthetic data maintains structure and perceptual consistency. Validate the data’s integrity and human-recognisable patterns through privacy metrics.

Structure and perceptual consistency metrics confirm that synthetic data retains schema, relationships and natural variation — all within the high-performance range.

#1.

Structure — Maintains format, schema and relationships

#2.

Perceptual — Preserves human-recognisable patterns and distributions

03.

Utility Performance

Measure the usability of synthetic data in analytics and AI workflows. Confirm that statistical distributions, diversity and quality align with real data.

All utility metrics-diversity, quality, and indistinguishability-meet high-performance benchmarks, confirming the dataset’s reliability for real-world applications.

SYNDATA benchmarks diversity, quality and indistinguishability against original datasets. Results confirm suitability for real-world use.

#1.

Diversity — Maintains real-world data variety

#2.

Quality — Matches original statistical distributions

#3.

Indistinguishability — Appears authentic to real data

03.

Utility Performance

Measure the usability of synthetic data in analytics and AI workflows. Confirm that statistical distributions, diversity and quality align with real data.

SYNDATA benchmarks diversity, quality and indistinguishability against original datasets. Results confirm suitability for real-world use.

#1.

Diversity — Maintains real-world data variety

#2.

Quality — Matches original statistical distributions

#3.

Indistinguishability — Appears authentic to real data

03.

Utility Performance

Measure the usability of synthetic data in analytics and AI workflows. Confirm that statistical distributions, diversity and quality align with real data.

SYNDATA benchmarks diversity, quality and indistinguishability against original datasets. Results confirm suitability for real-world use.

#1.

Diversity — Maintains real-world data variety

#2.

Quality — Matches original statistical distributions

#3.

Indistinguishability — Appears authentic to real data

03.

Utility Performance

Measure the usability of synthetic data in analytics and AI workflows. Confirm that statistical distributions, diversity and quality align with real data.

SYNDATA benchmarks diversity, quality and indistinguishability against original datasets. Results confirm suitability for real-world use.

#1.

Diversity — Maintains real-world data variety

#2.

Quality — Matches original statistical distributions

#3.

Indistinguishability — Appears authentic to real data

03.

Utility Performance

Measure the usability of synthetic data in analytics and AI workflows. Confirm that statistical distributions, diversity and quality align with real data.