Your AI needs examples. Your customers are not training material.
Future ATI creates, critiques, validates, and exports synthetic datasets for specialized AI workflows, evaluation, and edge-case testing.
The synthetic data pipeline
Every step from definition through export maintains quality and transparency.
Define
What kind of data? What domain? What edge cases?
Generate
Create synthetic examples using models and rules.
Critique
Model self-evaluation of quality and relevance.
Validate
Rule-based checks and consistency verification.
Dedupe
Remove near-duplicates and weak samples.
Review
Human sampling (automated + expert review).
Export
JSONL, CSV, Parquet, dataset card, manifest.
Always label synthetic content as synthetic. Every dataset includes a card documenting source, assumptions, and intended use.
Dataset types we generate
Synthetic datasets for training, evaluation, and edge-case testing across many domains.
Quality gates and dataset cards
Every dataset includes comprehensive metadata and provenance documentation.
Review tiers
Quality gates ensure synthetic data meets domain-specific requirements before use.
Automated checks
Format validation, length bounds, pattern matching.
Model critique
Self-evaluation for coherence, relevance, quality.
Rule validation
Domain-specific business logic checks.
Human sampling
Random sample review by trained operators.
Expert review
Required for healthcare, legal, finance, safety domains.
For high-sensitivity domains, expert review is mandatory before model training or deployment.
Deduplication example
Remove near-duplicates and keep the strongest samples.
Record A
"I need help with refund"
Record B
"Need help getting a refund"
Keep stronger sample
(more natural phrasing, better coverage)
AI safety evaluation suite
The strongest story: synthetic data finds where AI breaks before it reaches production.
Deployment blocked until resolved.
This is how we find safety issues before they reach your users.
Export formats and deliverables
Datasets export in formats that fit your pipeline.
Every export becomes a real deliverable with complete provenance. No generic blobs.
Healthcare, legal, and safety datasets
For sensitive domains, synthetic data requires special rigor.
Cite source basis
Document where each example originated or was inspired by.
Separate evidence levels
Mark clinical consensus vs. emerging research vs. educational use.
Mark traditional vs. evidence-based
Distinguish established practices from new approaches.
Avoid unsupported claims
No disease-treatment claims unless backed by evidence.
Require expert review
Mandatory review before model training or deployment.
Synthetic data systems cannot hallucinate authority. Professional judgment remains with qualified experts.