Flanks has integrated its regulated wealth data layer into Perplexity's connector ecosystem, enabling AI-driven portfolio and reporting workflows.
The partnership targets wealth advisors and financial institutions. The integration of Flanks' unified wealth data infrastructure into Perplexity system allows the latter’s answer engine and its agent platform users to query portfolio holdings, investment positions, and transaction history through natural language.
Usually, advisors hold client accounts across three to five banks, and data from legacy providers, spreadsheets and custodians is often inconsistent, particularly across Europe's fragmented banking sector. Flanks aggregates and standardises this data before it reaches an AI system, positioning itself as infrastructure that supports AI reasoning rather than a standalone advisory tool.
Integration into existing advisory workflows
The Flanks connector allows private banks and financial advisors to apply the aggregated data within functions such as client reporting, portfolio monitoring, client meeting preparation, and ETF overlap analysis, without leaving the Perplexity platform. Data is ingested automatically from more than 700 connected sources.
Joaquim de la Cruz, CEO and co-founder of Flanks, said financial advisors currently work with data spread across custodians, spreadsheets and legacy providers without a single consolidated source. He also mentioned that the partnership aims to connect AI to data across the full advisory process rather than to individual, isolated tasks.
Context and implications
Flanks is regulated as an Account Information Service Provider (AISP) by the Bank of Spain under the supervision of the European Central Bankand holds SOC 2 Type II and SOC 3 certification. The company also states it aligns with UK Consumer Duty requirements.
The partnership reflects a broader pattern in wealth management technology, where AI tools depend on the completeness and regulatory compliance of the underlying data before they can be applied to tasks such as reporting or portfolio monitoring. Furthermore, through the process of embedding regulated multi-institution data directly into an existing AI platform, the arrangement is intended to reduce the need for advisors to reconcile data manually across multiple systems before applying AI tools to client-facing tasks.
