Behavioural science, weighted scenarios, and a diagnostic engine that turns board questions into evidence, explanation, and prescription — on a named segment, with a measurement plan.
Three layers, deterministic in that order. AI enriches the prose; it never invents a finding.
Nine archetypes built on competence × ambiguity tolerance, validated through weighted scenarios — not personality tests. Blindspot detection compares what people say about themselves against what their answers actually reveal.
A library of root-cause hypotheses tied to design levers you can actually move. “Why do women convert worse?” produces a different diagnostic than “why do 25–34 year-olds stall?” — with the upstream cause named, not guessed.
A specific intervention on a named segment, with a measurement plan. We commit to a 28-day check: did the cohort move past the stage, by how much, and was the lever the right one. If not, we say so.
The profile is the start, not the end. Behavioural nudges move named cohorts in the background. MCP-ready integrations put survey signal, chat, and live portfolio data into one coherent advisor view.
Once we know the archetype and the blindspot, we deliver tiny, contextual interventions in the channels your client already uses — checking a balance, opening the app, reading the newsletter. Each nudge is measured. The next diagnostic sees what landed and what didn’t.
Our Model Context Protocol layer gives your AI copilot or human advisor every relevant signal at once: what the client revealed in the assessment, what their portfolio actually holds, what the conversation just surfaced. No copy-paste between tools. Better, faster investor support.
Three exploration surfaces we’ve shipped — designed around how humans actually navigate uncertainty, not how databases serialise. They’re examples, not the menu: we continuously design new ways for investors to engage with their money.
Eight hundred line items collapse into thirty stars, clustered by what the investor actually cares about. Decision fatigue drops; orientation appears. Choice architecture, applied to the fund universe.
The client picks what “distance” and “size” mean — ESG fit, ten-year return, drawdown, ticket size. The interactions are themselves a behavioural signal, fed back into the profile so the next session knows what they actually look at.
Each fund becomes a double helix coloured by its top SDGs, drillable down to constituents. Investors stop seeing tickers and start seeing impact. The shift from abstract to concrete is itself a confidence intervention.
Three surfaces built on the same logic as the diagnostic: visuals that respect how humans actually think, paired with nudges that arrive in the moments choices are made. The result is exploration that closes the intention–action gap instead of widening it.
If you have a board, a roadmap, or a quarterly conversion review — we’re built for you.
Research rigour first. AI amplification second. In that order, on purpose.
We measure how people behave under uncertainty — not what they say they’d do. The only way to detect the gap between intention and action.
We compare what a client believes about themselves with what their answers actually reveal — surfacing the gaps a Risk Tolerance Questionnaire never sees.
The classifier is rules-based and reproducible. Same answers, same result, every time. AI may write the narrative, but never the numbers.
Every prescription comes with a check-back. Did the named cohort move past the stage we said they would? If not, the hypothesis was wrong and we say so.
“We’re embedding u impact’s Investor Dating Profile inside our Wealth Activation Platform for Mirai 2026 — 1,200 professionals, June 2026.”
We’ll show you what the answer looks like — evidence, explanation, prescription — on a real cohort of yours.
Bring us the questionTell us about a real cohort and the question your board keeps asking. We’ll come back within a working day.