Company Profile • Fintech • Artificial Intelligence
Cashmere.
The AI-powered data layer for financial services - built to notice the moment someone gets rich, and hand that person to the right advisor.
The AI-powered data layer for financial services - built to notice the moment someone gets rich, and hand that person to the right advisor.
Here is a fact about wealth management that sounds like a rounding error and is actually the whole business: a single client can be worth $50,000 to $100,000 a year in fees and stay for two or three decades. Farbod Nowzad, who co-founded Cashmere and runs it, likes to point out that there is "essentially no other industry where you can acquire a non-enterprise client that generates $50k-100k annually and retains for 20-30 years." Once you accept that math, the entire game becomes: find that person, and find them before anyone else does.
The problem is that the person doesn't announce themselves. They become worth acquiring at a specific, often invisible moment - an inheritance clears, a business sells, a stock vests, a spouse passes. These are the wealth-triggering events, and historically the way advisors caught them was some combination of golf, referrals, and luck. Cashmere's bet is that the signal was sitting in the data the whole time, and that an AI pointed at the right sources could see the event as it happens.
So the company built a system that aggregates public and proprietary data, watches for those triggers, prioritizes the best-fit prospects for a given firm, surfaces the mutual connections that make an introduction warm rather than cold, and then drafts the outreach - all while staying inside the compliance rails that a regulated industry demands. It is, in the most literal sense, replicating human research at scale, then adding data that most firms simply cannot get to on their own.
That was the first act. The second is quieter and, if anything, more ambitious: Cashmere realized you cannot reliably act on a signal you cannot resolve. So it moved down the stack, toward the unglamorous work of turning fragmented customer records - scattered across the disconnected systems that every bank somehow accumulates - into a single, continuously updated "golden profile." The pitch on the homepage now reads less like a lead-gen tool and more like plumbing: the AI-powered data layer for financial services.
There's essentially no other industry where you can acquire a non-enterprise client that generates $50k-100k annually and retains for 20-30 years.- Farbod Nowzad, Co-Founder & CEO
Four steps sit between "a life event happened somewhere" and "an advisor sends a message the prospect actually wants to read."
Aggregate public and proprietary data; surface wealth-triggering events - inheritances, business sales, liquidity events - in real time.
Fuse fragmented records into one golden profile with a universal ID, enriched by internal and external signals.
Score propensity and next-best-action, then pair each prospect with the best-fit advisor and any mutual connections.
Generate personalized outreach, push it into the CRM, and keep it inside SEC-aware compliance rails.
Automates discovery, research and engagement of prospects for wealth firms - prioritizing best-fit leads, surfacing mutual connections, and drafting outreach that plugs into your CRM.
Entity resolution across disconnected systems into one continuously updated record, fusing internal and external signals with enterprise-grade validation.
Real-time surfacing of inheritances, business sales, liquidity events and life milestones - the moments that turn a stranger into a prospect.
Propensity-to-convert and next-best-action scoring built on a semantic ontology that models each firm's own business logic and relationships.
Plenty of startups say "AI for finance." Cashmere picked the least glamorous version of it - and that's roughly where the value hides. An illustrative sketch of where the hard work lands:
Note: the bars above are an editorial illustration of emphasis, not published metrics. What's verifiable is the direction - Cashmere moved from a point tool toward the data layer underneath it, because you can't act on a signal you can't resolve.
Previously founded the social audio platform Pludo and worked on anti-fraud machine learning at Lime. The throughline of his career isn't a product category - it's a willingness to build the infrastructure other people avoid.
A former machine learning engineer from Goldman Sachs, bringing the modeling and systems background needed to resolve finance's messiest records at scale.
After the seed, Cashmere added Sean Cheng - a Harvard PhD in Applied Physics - as Head of Machine Learning, a hire that tells you which end of the problem the company thinks is hardest.
Cashmere has hit on a fundamental challenge for wealth management firms in revolutionizing client acquisition.- Jeff Reitman, Canapi Ventures
| Legal Name | Cashmere AI, Inc. |
| Category | AI · Fintech · SaaS |
| Founded | 2023 |
| Headquarters | Culver City / Los Angeles, CA |
| Industry | Financial Services |
| Team Size | ~23 employees |
| Total Raised | $3.6M (Seed) |
| Lead Investor | Canapi Ventures |
Cashmere launches, targeting AI client acquisition for wealth management firms.
Closes a $3.6M seed round led by Canapi Ventures, with Benchstrength, Plug and Play, The House Fund, Courtyard Ventures and angels.
Hires Sean Cheng (Harvard PhD, Applied Physics) as Head of Machine Learning; onboards RIAs plus bank and wirehouse teams.
Repositions around an "AI-powered data layer for financial services" - golden profiles and entity resolution across bank systems.
Backers: Canapi Ventures (lead) · Benchstrength · Plug and Play · The House Fund · Courtyard Ventures · angel investors
Within roughly a year of launch, Cashmere reported about a dozen RIAs plus advisor teams at banks and wirehouses among its customers. Its site references work adjacent to names like M&T Bank and Wilmington Trust, alongside a "Top 10 US Bank" and a "Top 25 Global Bank."
The competitive neighborhood includes wealth-signal and prospecting tools such as Aidentified and WealthEngine, plus the general-purpose data-enrichment vendors financial firms already lean on. Cashmere's wedge is the combination: detect the trigger, resolve the record, and explain the match well enough that a compliance-bound advisor will actually make the call.
Before wealth signals, CEO Farbod Nowzad built Pludo, a social audio platform. The pivot is bigger than the category change suggests.
CTO Eshan Govil came from Goldman Sachs' ML desk; Nowzad from Lime's anti-fraud team. Both know messy data intimately.
Both founders were 27 when they closed the seed - a detail the trade press couldn't resist putting in the headline.
"Turn fractured client data into decision-ready intelligence" isn't marketing copy buried on a page - it's printed on the logo card itself.
Farbod Nowzad breaks down AI-driven client acquisition in finance on the "AI Series" podcast, and the trade press covered the seed round in detail.
Cashmere is a Los Angeles-based AI company building a data layer for financial services. It started as an AI-powered client-acquisition platform that helps wealth management firms spot high-net-worth prospects at the moment they experience a wealth-triggering life event - an inheritance, a business sale, a liquidity event - and then matches those prospects with the best-fit advisor and automates personalized outreach. The product has since expanded into resolving fragmented customer records across disconnected banking systems into a single, continuously updated 'golden profile' enriched with internal and external signals. Founded by Farbod Nowzad and Eshan Govil, Cashmere raised a $3.6M seed round led by Canapi Ventures in September 2024 and counts RIAs, banks and wirehouses among its customers.
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