Wealthtech company Flanks has launched its AI Financial Advisor, a wealth intelligence assistant designed to help advisers handle complex, multi-custodian client portfolios.
The tool uses a multi-agent AI framework to consider client wealth data alongside live market intelligence.
Flanks said advisers would be able to ask questions related to complex family structures, multi-custodian portfolios, and cross-border holdings to receive contextualised analysis across all asset classes.
AI Financial Advisor aims to improve wealth advisers’ decision making and efficiency, and operates under compliance guardrails, with descriptive analysis only.
Flanks develops and operates its AI capabilities in-house, using models it said were adapted and optimised for its specific wealth management use cases.
The assistant is underpinned by Flanks’ aggregation infrastructure, which connects to more than 600 financial institutions to deliver wealth data across asset classes.
Its capabilities include consolidated household and ownership-weighted views, real-time market sentiment benchmarked against client holdings, full cash flow and dividend income visibility, automated currency normalisation, and private equity capital call tracking.
"The wealth management industry has been asking the wrong question,” said Flanks chair and co-founder, Álvaro Morales.
“For the past couple of years, firms have chased AI-driven advice without fixing the data infrastructure beneath it.
“The AI Financial Advisor is what becomes possible when you finally get the data right: instant, trustworthy intelligence across every client, every asset class, every custodian, at a scale that was previously unimaginable.”
Flanks CEO, Joaquim de la Cruz, added: "We built the AI Financial Advisor on infrastructure that processes over 500,000 portfolios monthly across 600+ institutional connections in 33 countries.
“When your data foundation is this complete and this clean, the AI has something reliable to reason over. The data layer is the product; the AI is what it unlocks.”




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