Every twin is a real person.
Each twin is grounded in a real participant, built from multiple hours of AI-moderated and live video interviews. One real individual, traceable back to their interviews.
Every answer comes
with the receipts.
A digital twin's answer is sense-checkable. Click any number, drop into the underlying interview, see why the twin said what it said. Because for any decision leadership might be asked to defend, traceability is non-negotiable.
Why this matters
In regulated industries, pharma, finance, insurance, healthcare, the regulator does not care how clever the model is. They care whether you can show your work. Brox provenance is the closest thing in synthetic insights to a chain-of-custody.
How others do it
Internal LLM impersonation cannot do this. Because no real human was ever consulted, there is nothing to cite. Every answer is, by construction, an opinion the model produced from its training distribution.
Three things our competitors cannot say.
Every answer is sense-checkable.
When a twin says something unexpected, you can drop straight into the underlying interview transcript and see why. You can read the reasoning. You can ask "where did this come from" and there is an answer. Synthetic personas average their source out of existence. Prompted LLMs have no source at all.
Every divergence is a usable signal.
When a twin response diverges from what real customers actually do, you can see the divergence, interrogate it, and adjust without re-fielding a study. The twin's divergence is itself a usable insight, and it becomes training signal that tightens the next iteration.
Calibration, in public
We are constantly testing samples of recent Brox predictions against real respondent panellists, and against real-world outcomes where measurable. We publish these calibration reports on a quarterly basis.
Read the latest BenchmarkThe asset that compounds
Every traditional study ends when the deck is delivered. The data set goes onto a shared drive, the panellists go home, and the next question kicks off a new study from scratch.
Digital twins invert that. The panel persists, the data accumulates, and the next question runs against an asset that is materially smarter than it was last time it was queried.
Three vectors compound:
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Continuous enrichment
We re-engage real panellists on a regular cadence, capture recent decisions, refresh attitudes. The twin is always converging on the present, not stranded at the date the interview was first recorded.
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Behavioural models on top of the substrate
Models for purchase behaviour, switching, response under stress, adoption-curve dynamics, trust trajectories under crisis. These let us push the asset from "what would this individual say" to "what would this audience actually do."
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Steerable calibration
When you flag a divergence between twin response and reality, we trigger a follow-up with the underlying real panellists. Every gap between twin and real becomes training signal that tightens the next iteration.
Twelve months in, the asset is materially harder for anyone to replicate, because the calibration history is the moat.
Find out more
We could write pages and pages of marketing and sales fluff but instead we'd prefer just to show you the product.
Contact us and we'll set up a call, during that call you should come prepared with some business and research requests and we'll go through them together live. We'll also explain how we build our digital twins, why you can trust them, how to deploy them, how much it costs (it's a lot), how we validate them and anything else you fancy talking about. You'll see ROI immediately.
Currently live in the US, UK, Japan and Turkey and launching in much of the Middle East and APAC pretty soon.