Independent validation for synthetic and boosted survey data, from someone who doesn't sell the data. Hand me one dataset. In about a week, you know exactly what it can and can't support.
You send one dataset. Synthetic, boosted, or "digital twin" survey data, in whatever shape it's in. A short intake call covers what the study was supposed to measure and what decisions ride on it.
I stress-test it. I check the failure modes I've built and documented myself: variance collapse, central-tendency compression, inflated positivity, over-concentrated segments, and a real base too thin to carry the boost.
You get a memo and a call. A short, plain-language read on where the data holds and where it's likely distorted, including the specific cuts you can and can't defend to a stakeholder. Then we walk through it together and answer the "so can I actually use this" questions in real time.
Turnaround is about a week. One dataset, no retainer, no commitment. Your data stays confidential and is never used for anything else.
A vendor boosted your niche segments and the results look suspiciously clean.
You're evaluating a synthetic sample provider and need a second opinion that isn't theirs.
A stakeholder is about to make a real decision on augmented data, and it's your name on the deck.
The Sanity Check, scaled to a full study or tracker: every wave, every segment, a complete defensibility map of the dataset.
Validation checks and calibration standards built into your pipeline, so distorted synthetic data gets caught before it reaches a deck.
The pipeline work behind my own studies, applied to yours: automated processing, validation, and reporting for research operations.
Tell me what you're working with and what's riding on it. I'll tell you honestly whether the Sanity Check is the right fit, and what it costs. Currently taking on new validation work.