Lesley Li
Market and investor insight
More than a decade in financial services, connecting sustainable-investment intent with real investor needs.
Meet Lesley →Evidence. Explanation. Testable action.
Three connected layers take a team from structured investor signals to a change it can measure. Scores are calculated by fixed rules. AI-assisted explanations must stay tied to those results and pass factual review.
01
Profile
A versioned framework combining knowledge, self-report and scenario responses across competence × ambiguity tolerance. Nine profiles organise patterns for discussion. Questions, weights and thresholds are documented design choices; their validity needs to be evaluated for the intended use.
02
Diagnose
Compare cohorts, journey stages and calibration gaps. A curated library connects detected patterns with candidate explanations to investigate. Ranking reflects the configured rules, not a probability that an explanation is the cause.
03
Test
Design a specific intervention for a named segment, with an agreed outcome and review point. If the cohort does not move as expected, the evidence changes the hypothesis rather than being explained away.
Three ways to put the insight to work
Choose the route that matches the moment: improve a real investor journey, activate a live audience, or bring the thinking to your stage.
01 · Implementation
Build it into the investor journey
Apply the assessment to a defined cohort, diagnose patterns and turn the findings into interventions your team can test.
Explore implementation → 02 · Event activationLet the room experience it
Run a branded live assessment with private individual results and an anonymised cohort conversation.
Explore event activations → 03 · SpeakingPut investor behaviour on the agenda
Invite Lesley for a keynote, panel, workshop or hosting brief shaped around the audience in the room.
Explore speaking →After the diagnostic
Carry the question forward between diagnostics
For implementations that continue beyond the assessment, behavioural nudges and AI-assisted advisor support can carry the insight into the moments where decisions are made.
Always-on layer
Relevant support, tested over time
Use an assessment pattern to choose a support hypothesis, then test it in the channels your client already uses. A clearer comparison, a timely explanation or access to an advisor may help. Measure understanding as well as follow-through, and allow for an informed decision to wait.
One investor view
Chat, survey, portfolio: one advisor view
Bring assessment results, authorised portfolio data and conversation context into an advisor workflow. The available view depends on the systems connected and the permissions agreed. Model Context Protocol (MCP) provides an integration route; the project defines which sources the advisor can use.
Product experiences
Behaviour-aware fund exploration
Three examples of fund exploration designed to make comparisons more concrete. Each offers a different way into the same underlying information. Whether it improves understanding or helps a decision is a question to test with the intended audience.
Star Map
Funds as a navigable universe
Group funds into a navigable visual map, with the grouping criteria visible. Investors can explore a theme before opening the underlying fund information. Test whether this makes comparison easier; the visual format alone does not establish reduced decision fatigue.
Radar
What the client explores opens a conversation
Choose comparison axes such as historical return, drawdown or investment size, with definitions, dates and data coverage visible. With an agreed data arrangement, exploration can inform a follow-up question. Attention to a fund is not proof of a stable preference, suitability or intent to invest.
DNA Helix
Explore the holdings. Question the evidence.
Explore fund holdings through their mapped Sustainable Development Goals (SDGs), with a route to the constituent data. Show the mapping method, data date and coverage. SDG alignment describes an association with goals; it does not by itself demonstrate additional real-world impact.
Start with a decision people need to understand. Make the information easier to explore, then test what changes. Comprehension, comparison quality and the choice to seek help or pause belong alongside completion in the measurement plan. Explore the research briefs →
Built for the people asking the question
Wealth managers, fintech product teams and investment platforms: if you have a board, a roadmap or a quarterly conversion review, this is built for you.
Wealth managers & private banks
Investigate the needs behind investor hesitation. Test support for a defined cohort, measuring understanding, informed next steps and completion against an agreed comparison.
Financial-services product teams
Embed client profiling into onboarding. Segment nurture by archetype. Ship targeted journeys with a measurement loop instead of A/B guessing.
Distributors & platforms
Run board-grade diagnostics on a customer cohort, including activation gaps, drop-off stages and archetype skew, then export the intervention as a brief, not a slide.
Why this isn’t another quiz tool
Research rigour first. AI amplification second. In that order, on purpose.
Three sources of signal
Self-report, objective knowledge checks and standardised hypothetical scenarios contribute different evidence. No single question decides the profile.
Patterns worth investigating
We compare confidence with demonstrated knowledge, and stated comfort with scenario choices. The result is a possible calibration gap, not a claim about hidden truth.
Deterministic scoring
The classifier is rules-based and reproducible. Same answers, same result, every time. AI may write the narrative, but never the numbers.
Continuous calibration
We review the model after every survey project and at regular intervals as new aggregate data becomes available, updating it where the evidence supports a change.
See the thinking behind the product
Trust should not depend on a claim alone. Explore the method, the people responsible for it, and the evidence that informs our work.
Methodology
What we measure and what we do not
Inputs, scoring, calibration, AI boundaries, limitations and appropriate use.
Explore the method → LearnFinancial investment behaviour, with its evidence attached
How knowledge, confidence, uncertainty, trust and the decision environment interact.
Read the field guide → PeopleResearch, finance and delivery experience
Meet the founders responsible for turning the research into a working assessment.
Meet the team →Built by people who can challenge each other
Market insight, research architecture and delivery discipline in one founding team.
Dr Clemens Chaskel
Platform and research
Global innovation strategy, applied research at the Institute for Manufacturing, University of Cambridge, and experience founding InnovationFlow.
Meet Clemens →
Alistair Hume
Delivery and methodology
15+ years delivering finance, data, systems and regulatory change across banks and start-ups.
Meet Alistair →Ongoing partnership
Partnering with NTT DATA
We went live together at Mirai 2026 in London, bringing investor profiling to a live audience. Our partnership continues beyond the event as we explore how behavioural insight can support more relevant and engaging financial experiences.
Mirai 2026 milestone · 9–10 June · London
Bring us a question your board keeps asking.
We’ll show you what the answer looks like through evidence, explanation and a testable next step, using a real cohort of yours.
Bring us the question