The investment will drive rapid product development and strategic hiring – including a newly-appointed Head of Machine Learning.

Co-founded by Farbod Nowzad and Eshan Govil, Cashmere leverages the state-of-the-art in AI to automate the discovery, research, and engagement of prospective clients for wealth management firms at scale.

Its launch coincides with the ongoing ‘Great Wealth Transfer’ globally. According to data from Knight Frank, $90 trillion in assets is set to be transferred from Baby Boomers to their heirs and beneficiaries in the US alone over the next two decades – a shift which could make millennials the richest generation in history.

Despite this unprecedented ‘inheritocracy’ and over $60 trillion assets under management already today, the wealth management sector has a major client acquisition problem. From small, independent firms to behemoths like Goldman Sachs and Morgan Stanley, the industry lacks reliable systems to build and nurture high-net-worth sales funnels.

Farbod Nowzad, Cashmere CEO and Co-founder, commented: “There’s essentially no other industry where you can acquire a non-enterprise client that generates $50k–100k per year in revenue and sticks around for 20–30 years. Despite this, the industry does not have the infrastructure and intelligence layer to consistently identify who the best clients are, match them with the right advisor at the right time and begin the trust-building process. By relying on referrals and dated processes, firms are leaving a lot of money on the table. We built Cashmere from first principles to address this.”

The seed round was led by Canapi Ventures, a specialist investor in the fintech space, with participation from Benchstrength, Plug and Play, The House Fund, Courtyard Ventures, and several angel backers. As the lead investor, Canapi Ventures will act as board observers, providing strategic guidance as Cashmere scales its operations.

Jeff Reitman, General Partner at Canapi Ventures, said: “Cashmere has hit on a fundamental challenge for wealth management firms in its mission to revolutionise client acquisition. Its platform equips firms with the intel they need to efficiently acquire and retain high-value clients, and their team has already begun working with advisor teams at some of the largest banking platforms. We believe that Cashmere’s technology will not only transform how wealth management firms operate but set a new standard for client acquisition across the industry.”

As Cashmere expands its footprint in the wealth management industry, it seeks to bring the same technology and infrastructure to other verticals of financial services with similar client acquisition motions.

The capital injection has already been used to make a strategic hire in Sean Cheng – a Harvard PhD graduate in Applied Physics – as Head of Machine Learning. Cheng bolsters the existing data science expertise of the business’ leadership team. Cashmere CEO, Nowzad, was the first data science graduate from UC Berkeley, and CTO Govil is a former machine learning engineer from Goldman Sachs.

Cashmere today identifies when ideal customers are likely seeking wealth management services and matches them with best-fit advisors. This includes capturing information on major life events, replicating human research at scale and augmenting this with data not readily accessible for most firms. Cashmere’s platform adheres to the rigorous standards of compliance and data security in the financial industry, and can be leveraged through a standalone dashboard or customised and deployed within a firm’s private ecosystem.

Key features include:

• Optimal Matching: Identifies and prioritises the best prospects for each firm.
• Mutual Connections: Discovers potential mutual connections between advisors and prospects.
• Personalised Outreach: Generates and automates tailored content from advisors to specific prospects, for improved engagement.
• Integrations: Cashmere can plug in with an existing CRM to monitor lead lists that firms may already have, and improve discovery through client-similarity modelling.