Technology Designed To Think With You

Last month, Addepar unveiled Addison, an AI assistant integrated into its platform that lets users interact using natural language.

It’s a useful feature, but not unique to Addepar: Masttro unveiled theirs last year as part of Masttro Intelligence, and earlier this year Unlimited.ai introduced their own, named Laila.

The reality is that embedded AI assistants that allow natural language interaction look set to become standard across software, financial or otherwise. 

While smaller competitors may be more nimble with releasing new features, Addepar remains the industry benchmark, the platform most competitors measure themselves against.

Established in 2009 by Joe Lonsdale (also a Palintir founder) and Jason Mirra, they’ve grown to dominate the space since.

Addepar currently serves over 1,400 companies across 60 countries, from single family offices to RIAs, and private banks, with the platform managing $9T of assets. Last year they were valued at $3.25 billion.

Like similar platforms, Addepar consolidates both public and private markets data to give family offices a unified view, and they aim to deliver a complete solution in one platform.

We spoke with Addepar Chief Technology Officer Bob Pisani to understand where family offices get the most value from it.

“For family offices, the challenge isn’t a lack of tools, but a lack of consistency across them. Fragmented data leads to disconnected workflows and makes it harder to act with confidence.

“By grounding everything in a permission-aware, fully traceable data layer, we ensure reporting, analysis and AI-driven capabilities are all built on complete, trusted data.”

Addepar is a powerful tool but only useful if set up correctly. Implementation is critical, and Pisani says this starts with understanding how a client firm operates.

“Family offices often manage complex portfolios with lean teams and highly customized structures, so the focus is on establishing a clean, reliable data foundation and ensuring the platform reflects their operating model in practice.”

He notes that where implementation has evolved the most is in how quickly teams can realize value.

“AI is accelerating how data is ingested and structured, but more importantly, it’s lowering the barrier to using the platform day to day—making sophisticated analysis accessible from the first interaction.”

A helpful evolution considering that, most wealth management platforms rely heavily on skilled professionals to operate effectively, something reinforced by the head of automation at a prominent multi-family office we spoke with.

They noted how Addepar was the industry standard for reporting, yet still only as good as the analyst using it: “It’s like having a fancy set of kitchen knives or cookware, you aren’t suddenly going to be Gordon Ramsay.’

Addepar manages an enormous amount of data, and Pisani says more than 40% of assets on platforms are Alternatives. In a segment that has historically lacked consistent, comparable benchmarks, the company uses this data to provide a solution.

“Private Fund Benchmarks address that by leveraging aggregated and anonymized data from across the Addepar platform to create quarterly and since-inception benchmarks, spanning approximately 14,800 funds, 4,900 managers and more than 115,000 underlying LP positions.” 

He says it uses contributions, distributions and valuations to calculate IRR in a way that reflects actual LP outcomes.

“Because these benchmarks are integrated directly into the platform, family offices can evaluate fund performance, understand relative positioning and analyze drivers of return in the context of their own portfolios.”

While family offices can’t compare themselves directly with others, they can assess their portfolios against broader industry data without exposing any individual firm’s information.

“That context is applied directly within the portfolio, enabling firms to understand relative positioning, identify trends, and assess how their investments are evolving over time.”

Addepar uses the same aggregated and anonymized dataset to simulate scenarios and forecast cash flows.

“By applying historical cash flow data across thousands of funds, family offices can model capital calls, distributions and liquidity needs over multiple time horizons—whether that’s one, three or five years out and beyond.”

“That allows teams to move from backward-looking analysis to proactive planning—aligning investment pacing with liquidity needs, preparing for major life events or expenditures, and making more informed decisions about where and when to deploy capital.”

Amid the many platform features, Pisani believes the AI assistant Addison will be particularly useful to leaner-staffed family offices, bringing workflows together and reducing fragmentation. 

“In practice, that includes everything from analyzing performance, exposures and liquidity to supporting investment decisions and reporting to family stakeholders, all within a consistent, governed environment.”

“Over time, this will extend through more proactive insight delivery, with Addison surfacing risks, trends and opportunities in context, and beginning to drive more operational workflows.” 

Pisani adds that Addison will support workflows but still keep human judgment at the center.

“These workflows remain fully governed and permission-aware, with transparency into how outputs are generated. Actions are not taken autonomously; teams review and decide how to proceed.”

The goal is to accelerate execution, not replace decision-making. It reduces time spent gathering information and allows teams to focus on judgment and outcomes.

And while Pisani believes AI is fundamentally changing how work gets done, he doesn’t see it replacing the role of human judgment.

“For family offices, decisions are rarely just analytical. They reflect a combination of long-term objectives, risk tolerance, governance structures and deeply personal considerations that evolve over time. Advice in that context is not a model. It requires interpretation, perspective and accountability.”

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