Turning modeled intelligence into decisions firms can trust

Cashmere is a people-intelligence and data-enrichment platform for financial firms. It was already live with real customers when I joined, so my job was to mature a product, not invent one. Our design partner was a wealth management firm, which gave me a concrete, high-stakes user: an advisor deciding who to pursue and how to reach them.

Before adding anything, I studied how the platform managed and surfaced its data, the lists, tables, and scaffolding under every workflow. That read set the strategy for everything that followed.

Two problems sat under almost everything. Trust: our wealth figures are modeled estimates, and a number shown the wrong way could quietly cost the platform its credibility. Clarity: the product asked users to move between genuinely different jobs, and when those blurred together people reached for the wrong tool. Everything I shipped came back to these two.

The platform ran one search for two opposite jobs: resolving a known person from a LinkedIn URL or email, and surfacing new prospects from criteria. I split it into named Lookup and Discovery workflows and let the required fields reinforce the split, so the form itself tells you which job you’re in. Discovery climbed from single digits to a peak of 273 weekly searches, overtaking the legacy search it replaced.

THE PROBLEM: Our wealth figures are modeled estimates, not numbers from a bank statement. Stakeholders wanted a hard number to ground a prospect, but net worth is genuinely hard to pin down, and a single confident figure implies a precision we do not have. Show it wrong and an advisor stops trusting the platform.

THE SOLUTION: I worked closely with our design partner to land on a base and a range. A conservative estimate sets a floor we can stand behind from observed assets and income, and a modeled projection shows the realistic upper range from peer and demographic data. The two are labeled distinctly and paired with how complete the underlying data is, so an advisor gets the number they asked for without mistaking an estimate for a fact.

THE RESULT: Advisors got a figure they could actually ground a conversation in, and the base-and-range treatment held up across every wealth tier, from mass affluent to exceptional.

Beyond the headline workflows, I sharpened the surfaces users live in daily: bulk CSV enrichment so a whole list is matched and enriched in one motion, customizable result tables sorted by what actually drives a decision, and saved searches so a strong prospecting query can be reused instead of rebuilt.

A saved search is really a saved set of filters, so where it should live was the real question. I explored a few placements, each anchored by the active search name.

What shipped took a different cue. The drawer pulls out from the filters with the active search name at the top of the filter panel, because what you are saving is the filter configuration. Keeping it with the filters made that relationship obvious.

The old brand still looked like a generic SaaS website, leaning on bright orange and dark blue that begged for attention. I took a more principled route and used the name itself to ground the identity in natural, earthier tones that evoke trustworthiness and humanness. Cashmere is a fiber spun in the high mountains of the Tibetan Plateau and Mongolian Steppe, regions defined by patience and quiet strength, so I rooted the brand there rather than reaching for another generic look. Mountains carry permanence and trust, topographic lines turn layered data into visible hierarchy, and the cashmere goat keeps it human. The real challenge was balancing those natural tones with a modern technology feel, so the brand stayed capable and current while feeling trustworthy and human in a world of AI.

Discovery adoption climbed, the wealth work landed as decision-support advisors actually used rather than features we guessed at, and the modeled insights proved valuable well beyond our first partner, expanding from one wealth manager to financial institutions broadly.

The hard part wasn’t adding features. It was making dense, uncertain intelligence something people would trust and act on, and making each workflow unmistakable. Coming into a live platform meant designing around real constraints and earning trust one decision at a time. And focused work travels: solving one firm’s problem deeply is what made it valuable to institutions broadly.