// hybrid_capability_centers
Find out where your synthetic data stops transferring.
Synthetic data is cheap to scale and easy to over-trust. Datafy Lab measures how your synthetic datasets transfer to real-world performance, surfaces the gaps, and recommends the real data that closes them.
// the_gap
Synthetic ≠ real until you measure it.
Models trained heavily on synthetic data often look strong in evaluation and fail in deployment. We quantify the transfer gap so you know where synthetic helps and where real data is required.
synthetic_validation.deliverables
01Synthetic vs real performance delta
02Transfer-gap map by scenario
03Domain-gap analysis
04Real-data collection recommendations
05Blended dataset plan
// how_it_works
Measure the gap, then close it.
// 01
Baseline
Measure model performance on held-out real-world data.
// 02
Compare
Quantify the delta between synthetic-trained and real performance.
// 03
Localize
Map which scenarios and domains the gap shows up in.
// 04
Close
Recommend targeted real capture to close the highest-impact gaps.
// faq
Common questions
Measuring how a model trained on synthetic data performs on real-world data, mapping the transfer gaps by scenario and domain, and recommending the real data that closes them.
We focus on validating and blending synthetic data and measuring transfer to real performance, rather than generating volume for its own sake.
By comparing performance on held-out real data against synthetic-trained baselines, broken down by scenario and domain so the gap is actionable.
Yes. Validation runs on the synthetic and real data you already have, then we recommend targeted real capture to fill the gaps.
Each qualified dataset ships with a Model-Ready Data Certificate that reports the synthetic-vs-real split alongside coverage, balance, and readiness.
Book a Data Failure Audit or share your synthetic and evaluation data so we can scope a transfer-gap analysis.