When people ask, “Can synthetic research be trusted?” they’re solving the wrong problem.
The right question is, “Was it properly calibrated and validated?”
We don’t let an LLM guess the market. We ingest census data, FAOSTAT and Comtrade trade data, retail scanner signals, menu trends, past studies, and price audits. We build synthetic cohorts that match real distributions.
We validate against anchors. Document confidence bands. Keep local experts in the loop. We even allocate grumpiness scores to individual digital twins because real populations include people who hate everything.
Peer research from Stanford, Cambridge, and others shows well-calibrated synthetic outputs can match in-market samples on key measures.
Credibility comes from method, not marketing.
Bad synthetic research is garbage. So is bad traditional research.
Good synthetic research, built on rigorous methodology and validated against real behavior, produces results that look the same as human panels.
The question isn’t whether AI can do research. It’s whether you’re using the right methodology.
Most skepticism I hear comes from people who tried ChatGPT and got mediocre results. That’s like using a calculator wrong and concluding math doesn’t work.
Synthetic research done right isn’t a replacement for expertise. It’s a force multiplier.
But only if you do it right.