How RIAs Can Cut Investment Research Time by 90%

How RIAs Can Cut Investment Research Time by 90%

Investment committee prep shouldn’t take all week. An AI investment platform for RIAs can turn a client question into data, analysis, an action framework, and client-ready language in one workflow.

One Client Email Can Create Hours of Work

Imagine an advisor opens this email from a client named Gaston:

“I keep hearing value stocks may be better positioned than growth right now. Should we tilt more of my portfolio toward value, which ETFs should we consider, and what do valuations, earnings growth, interest rates, inflation, market breadth, and recent performance say about value versus growth today?”

Simple question. Not a simple answer.

To respond responsibly, the advisor may need to compare valuations, earnings growth, ETF performance, volatility, drawdowns, sector exposure, interest rates, inflation, market breadth, fund fees, and tax implications.

Then comes the harder part: deciding what the evidence actually means and explaining it in language the client can understand.

That can turn one email into hours of research.

The problem isn’t that RIAs lack investment expertise. It’s that too much of that expertise gets consumed gathering data, moving between tools, building spreadsheets, reconciling conflicting signals, and writing the final response.

DeepVest AgentLab changes where the advisor spends that time. Instead of starting with research collection, the advisor can start with the question.

The Prompt We Put Into DeepVest AgentLab

My client Gaston emailed me: "I keep hearing value stocks may be better positioned than growth right now. Should we tilt more of my portfolio toward value, which ETFs should we consider, and what do valuations, earnings growth, interest rates, inflation, market breadth, and recent performance say about value versus growth today?" Using current market data, answer Gaston with specific numbers and compare representative value and growth ETFs on valuation, earnings growth, sector exposure, performance, volatility, drawdowns, and sensitivity to the current macro environment. End with the action you think we should take in his portfolio, including the size of any value tilt, ETF options, key risks, what would make us reverse the decision, and a clear client-ready response I can send him.

What DeepVest Returned

Schedule a demo with DeepVest to see how AgentLab can turn an advisor's investment question into multi-factor research, an action framework, and client-ready language.

What Would This Research Take Manually?

Look at what happened between Gaston’s email and the final answer.

The advisor needed ETF-level valuation data for Vanguard Value ETF (VTV) and Vanguard Growth ETF (VUG). Then earnings-growth estimates. Dividend yields. Volatility. Maximum drawdowns. Three-year and one-year returns. Sharpe ratios. Sector exposures. Fund fees.

That was only the investment side.

The rate thesis also had to be tested against Treasury yields rather than repeated because it sounded plausible. Inflation sensitivity had to be examined. Market breadth needed context. Conflicting findings between specialists had to be reconciled.

Then the advisor still needed to answer the actual client question:

What should we do?

A manual workflow can mean several research sites, ETF fact sheets, a market-data terminal, spreadsheets, calculations, and then another round of work translating everything into a client email.

DeepVest’s AgentLab performed those tasks inside one research workflow within minutes.

The important distinction isn’t “AI versus advisor.” It’s collection versus judgment. The advisor can spend less time collecting numbers and more time deciding whether those numbers belong in Gaston’s portfolio.

Where the 90% Comes From

The 90% in the headline is best understood as workflow compression, not a universal performance guarantee. Consider the research sequence above. A thorough manual review can easily consume several hours when an advisor must source, calculate, cross-check, interpret, and write everything independently.

AgentLab changes the starting point. The research, comparisons, calculations, conflicting evidence, risks, reversal conditions, ETF options, and draft client communication arrive together for advisor review.

That can turn a multi-hour research project into a review-and-decision workflow measured in minutes rather than hours. The advisor still has important work to do. DeepVest itself flagged what it didn’t know: Gaston’s holdings, tax status, risk tolerance, and time horizon. Those inputs determine whether the proposed tilt is appropriate.

That’s a feature of good research, not a weakness. The system doesn’t merely produce an answer. It shows the advisor where the answer stops.

What an AI Investment Platform for RIAs Should Actually Do

Speed by itself has little value if it produces shallow research. The more useful standard is whether an AI investment platform for RIAs can compress the mechanical work while preserving the reasoning an advisor needs to make a defensible decision.

In Gaston’s case, DeepVest did several things at once:

• Tested the client’s premise rather than agreeing with it.
• Found that value was cheaper, less volatile, and producing more income.
• Found that the interest-rate argument Gaston had heard wasn’t supported by the data.
• Preserved the case for growth.
• Identified what could reverse the recommendation.
• Converted the research into language an advisor could actually use with a client.

That’s more valuable than generating another market summary. It’s research organized around a decision.

Faster Research Should Create Better Conversations

Clients don’t care how many browser tabs their advisor opened. They care whether the answer is thoughtful, specific, and relevant to them.

“Value looks attractive” is generic.

“Value trades around 17 times forward earnings versus 28 times for growth, has shown roughly half the volatility, but the interest-rate thesis you heard doesn’t hold up in the data” is a different conversation.

Hard numbers create clarity. Caveats create credibility. Speed matters because Gaston doesn’t need to wait several days for his advisor to begin researching a question he asked this morning.

DeepVest gives the advisor analytical leverage. The advisor decides how to use it. That’s how investment research becomes less about gathering information and more about applying judgment.

Schedule a demo with DeepVest to talk with our team about how AgentLab can support your firm's investment research and client-response workflow.

For questions, contact: [email protected]