Sifar builds model-ready training datasets for AI and ML teams. Real-world sourced, synthetically expanded, and delivered with a statistical fidelity report so you know exactly what you are working with.
Most AI teams hit the same wall. The model architecture is solid, the infrastructure is ready, but the training data is sparse, skewed, or missing the edge cases that matter. Sourcing and cleaning real-world data takes weeks. Building it from scratch takes longer.
We start with permissively licensed datasets that match your domain. Not scraped noise. Structured, relevant records that reflect the actual distribution of your problem space.
We expand the source data to the row count and feature coverage you need. Edge cases, underrepresented classes, rare event signatures. The scenarios your model needs to see before production.
Every delivery includes a full statistical audit showing how closely the synthetic data mirrors the source across every feature and combination. You know exactly what you are getting.
A 15-minute call is enough to scope your use case, feature requirements, row count, and edge case priorities.
We identify permissively licensed real-world datasets that match your domain and curate the records relevant to your model.
Using state-of-the-art generative models, we expand the source data to your required volume with full edge case and feature coverage.
You receive a clean, structured dataset alongside the Fidelity Report. Every metric, every feature, fully verified before it touches your pipeline.
Tell us your use case and we will build you a free 100-row sample within the week. No commitment, no strings. If it looks right, we go from there.
We take your data specification and turn it into a model-ready dataset. Every engagement follows the same process: understand the use case, source the right real-world foundation, expand synthetically, and verify the output meets a rigorous statistical standard before it reaches you.
We start with permissively licensed datasets that match your domain. Not scraped noise, not generic public datasets thrown into a folder. Structured, relevant records that reflect the actual distribution of your problem space.
We use state-of-the-art generative models to expand the source data to the row count and feature coverage you need. Edge cases, underrepresented classes, rare event signatures. The scenarios your model needs to see before it ever touches production.
Every delivery includes a full Fidelity Report. Not a summary paragraph. A complete statistical audit showing how closely the synthetic data mirrors the source across every feature and feature combination. You will know exactly what you are getting before you train anything on it.
This is what separates Sifar from a data vendor that hands you a CSV and calls it done. The Fidelity Report is a full statistical breakdown of synthetic data quality. Here is what it covers.
How well the synthetic data replicates the statistical properties of the source across individual features and feature combinations. We measure accuracy at three levels of complexity.
Univariate accuracy measures how closely each individual feature's distribution in the synthetic data matches the source. Bivariate accuracy measures how well pairwise relationships between features are preserved. Trivariate accuracy captures three-way feature interactions, the level at which most synthetic data products begin to degrade.
| Feature | Univariate | Bivariate | Trivariate |
|---|---|---|---|
| province | 99.7% | 97.9% | 96.0% |
| urban_rural_classification | 99.6% | 98.0% | 96.3% |
| primary_store_format | 99.2% | 97.9% | 96.1% |
| household_size | 99.2% | 98.0% | 96.3% |
| age_band | 99.1% | 97.1% | 95.3% |
| basket_size_avg | 98.8% | 97.7% | 96.4% |
| visit_frequency_monthly | 98.8% | 97.3% | 95.6% |
| avg_monthly_spend_cad | 98.3% | 97.2% | 95.5% |
| churn_risk_label | 98.0% | 97.4% | 96.3% |
| promotion_sensitivity | 97.9% | 96.9% | 95.4% |
| loyalty_program_member | 97.4% | 97.0% | 95.9% |
| Total | 98.7% | 97.5% | 95.9% |
Synthetic data that preserves individual feature distributions but breaks the relationships between them is useless for training.
The correlation section shows the full correlation matrix for both the original and synthetic datasets side by side, along with a difference matrix that visualizes any drift. If the relationships in your data are preserved, the difference matrix is nearly empty.
That is the standard we hold every delivery to.
Every feature in the dataset gets its own distribution comparison chart. Original versus synthetic, overlaid, for both the continuous distribution and the binned representation.
Each chart is labeled with its per-feature accuracy score. You can see at a glance whether household income banding, visit frequency, age distribution, or any other feature has been faithfully reproduced.
Feature interactions are where most synthetic datasets fall apart. The bivariate section shows joint distribution plots for the most analytically significant feature pairs.
Spend versus income band. Churn risk versus loyalty program membership. Promotion sensitivity versus visit frequency. Each pair is shown for original and synthetic side by side so you can verify the relationships in your data survived the synthesis process.
Does the synthetic dataset occupy the same region of the feature space as the original? We measure this using cosine similarity and a discriminator model.
Cosine similarity near 1.0 indicates the synthetic data is directionally aligned with the source in high-dimensional space. Our recent delivery achieved 0.99880.
The discriminator AUC measures whether a trained classifier can reliably distinguish synthetic records from real ones. An AUC near 50% means it cannot. Our recent delivery scored 54.7%. At that level, synthetic and real records are statistically indistinguishable to a trained model.
The distances section tests for two failure modes that would make synthetic data risky in production: overfitting to the training set, and failure to generalize.
Distance to Closest Record (DCR) measures how close each synthetic sample is to its nearest real record. DCR Share measures what percentage of synthetic samples are closer to a training record than to a holdout record. A value near 50% indicates the synthetic data generalizes equally well to both.
Our recent delivery achieved a DCR Share of 49.8% and an NNDR Ratio of 1.363, consistent with strong generalization.
When you receive a dataset from Sifar, you are not being asked to trust us. You have the numbers. You can verify the accuracy per feature, inspect the correlation preservation, check the discriminator AUC, and confirm the distance metrics before a single row touches your training pipeline. That is the standard every delivery is held to.
Tell us what you are working on and we will get back to you within one business day. If you are not sure what you need yet, a 15-minute call is enough to scope out what a dataset would look like for your use case.
We keep the process simple. No lengthy intake forms, no lengthy sales cycles.
15 minutes to understand your model, your current data situation, and what you are trying to build.
A free 100-row dataset built to your specification, delivered within the week. No commitment required.
If the sample looks right, we agree on specifications, timeline, and delivery for the full dataset.
We respond within one business day. Your information is never shared.
We will be in touch within one business day. In the meantime, reach us directly at Sales@SifarLabs.com.