Pure Benchmarks Introduces a New Category of Retail Investor Data

A continuously evolving dataset that benchmarks every investment decision — built to empower investors, not monetize their data.

Pure Benchmarks today highlighted the underlying data architecture behind its retail investor benchmarking platform, distinguishing its approach from a wave of recent announcements in which large technology companies have begun allowing users to link financial accounts to AI assistants.

When major AI platforms introduced account-linking capability for personal finance this year, the development was widely framed as a step toward democratized financial advice. Industry observers have noted a different read: linking financial data to a large language model does not, on its own, generate new data about an investor’s actual financial outcomes. It primarily gives platforms a more precise way to understand the people whose data they now hold — data that can, in turn, support advertising and other revenue models built around that information.

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Pure Benchmarks takes a different approach. The platform uses AI not to monetize connected account data, but to deliver a category of investor-facing data that has never existed before — translating raw performance information into tools built specifically to empower the retail investor, not to generate ad revenue or resell that data to third parties.

Pure Benchmarks was built around the premise that retail investors should have access to a category of performance data that has never existed in the consumer market, generated independently of any platform with a commercial interest in influencing what an investor buys, sells, or holds.

The platform’s data architecture is built on two components.

The first is a continuously updating record of decision-level outcomes. Every trade, reallocation, or strategy change made on a connected account — whether directed by a financial advisor or made independently by a self-directed investor — is benchmarked against the outcome that account would have produced had that decision not been made. Investors relying on a financial advisor often have no way of knowing whether that advisor’s decisions are producing growth or creating loss over time. Self-directed investors face the same blind spot with their own decisions. The result is a living dataset — not a static snapshot — that allows an investor to benchmark whether the decisions made on their account, by themselves or an advisor, have actually maximized their performance.

This data engine is powered by AI that operates entirely within the platform’s own walls. Unlike AI models trained on broad, general-purpose data, the system here is purpose-built to enhance one specific dataset — the data generated by Pure Benchmarks’ own users — so that investors can avoid the noise that often leads to emotional selling and other costly decisions, and instead make better decisions and obtain better outcomes.

The second is a real-time peer benchmark spanning nine standardized asset allocation categories. Verified, anonymized performance data from connected accounts is aggregated daily, allowing an investor to see how their portfolio is performing relative to others in the same risk category — both within their own financial institution and across the broader market.

“Most of what passes for financial data today is a snapshot frozen at a single point in time,” said Shawn Tierney, Founder of Pure Benchmarks. “What we built is different. It is a living, breathing, evolving dataset that becomes more valuable with every single investor who joins.”

Pure Benchmarks does not function as a budgeting tool, a portfolio tracker, or a source of personalized financial advice. The platform is positioned as a data company, generating benchmarking data not currently available elsewhere in the retail investing market — and translating that data into actionable tools investors can use to make more informed, decisive choices about their financial future.

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