MNAI Digital Panel
Completed interviews, surveys, and focus groups, rebuilt as a persistent panel of Digital Twins. Validated against what your customers already said.
Built from your own research · Managed engagement
Research ends with a transcript, a presentation, and a set of findings. Then the participants go home.
Six months later the product changes, a competitor launches, pricing comes up for review. The people who could answer are no longer reachable.
The study answered the questions you had in the quarter you ran it.
Digital Panel gives it a longer operating life.
Individual-level fidelity is published. Panel-level fidelity is measured on your study.
On Twin-2K-500, the Mnemonic Digital Twin predicted the answers of 2,058 real people across 191,406 held-out questions, reaching 89% of the human accuracy ceiling. Above the ~85% published reference.
How closely a reconstructed panel reproduces your room depends on your participants, your discussion guide, your category, and what survived in your archive.
Panel fidelity is a property of your study, not a property of our model. So we measure it on your study, before the panel is used for anything else.
Not every completed study can be validated. You find out first, not after.
We review what survived before anything is built or committed.
You are in an unusual position with a completed study. You know what the humans said.
Twins are constructed from participant profiles and earlier material. Original responses are held back. The panel answers selected questions from your discussion guide, and its output is scored against the real transcript.
The method behind the published benchmark, applied to your archive instead of ours. Construct from earlier data, hold out later answers, score against reality.
Deliverable
A project-specific fidelity assessment, before the panel explores new territory.
New questions and new stimuli, against a panel with a known error rate.
Which benefits carry the decision, and where adoption breaks down.
“Which of these three formulations would our existing customers switch to, and which would not switch at all?”
Which language produces interest, trust, confusion, or resistance, and for whom.
“Does the sustainability claim strengthen or weaken intent among customers we recruited on performance?”
Prices, bundles, and subscription mechanics against a base you have already characterized.
“Would our current subscribers accept an annual-only tier, and which cancel instead of converting?”
Reactions to changes in onboarding, service, purchasing, and retention.
“Where in the redesigned returns process do customers who already complained give up?”
Which hypotheses justify new human fieldwork, and which can be dropped first.
“Of these nine concepts, which three are worth putting in front of real people next quarter?”
The output is the structure of the room, not one synthetic customer.
Every question returns:
How responses are elicited
Responses are elicited independently, one twin at a time, then analyzed as a set. We do not simulate twins persuading each other. Modeled group dynamics cannot be scored against your archive, which makes them the part of any synthetic panel most likely to be wrong and least likely to be caught.
A panel can only be built from people you have already talked to.
Your customers, from your data, not a synthetic sample of a country.
A way to reach that research with better questions and fewer candidates.
One completed study. One defined business question.
2 weeks from receipt of materials·Panel queryable for 6 months·From $6,000
Further rounds scoped separately. Mnemonic runs construction, validation, simulation, analysis, and reporting. You provide the archive and define the question.
Verbatim customer speech, treated as such.
Tell us what you hold and what you need to understand next. If your archive cannot support a fidelity assessment, we will tell you.
The assessment asks four things
