That's exactly the problem Quoin was built to solve.

Artificial intelligence promised to help. But the first wave of AI tools created a new problem: hallucinations. Confident, plausible-sounding answers that were simply wrong. For an RIA with fiduciary obligations, a hallucinated fact in a due diligence report isn't a minor inconvenience - it's a liability. The SEC has been direct about this risk, flagging AI accuracy failures as a specific concern for investment advisers operating under fiduciary duty.

What "Agentic" Actually Means - and Why It Changes Everything

There's a lot of noise around the word "agentic" in AI right now. In Quoin's case, it has a specific and meaningful definition: rather than generating a response from a single prompt, Quoin's research engine autonomously plans, queries, cross-references, and synthesizes - breaking complex due diligence questions into discrete research tasks and executing them in sequence.

The practical result is research that shows its work.

Every claim in a Quoin report is tied to a source. Every figure is traceable. The system doesn't speculate - it cites. If a data point isn't substantiated, it isn't included. For RIAs who need to defend their process to clients, CCOs, or regulators, this distinction matters enormously.

This isn't a chatbot with guardrails bolted on after the fact. The citation-first architecture is foundational to how Quoin generates research - which means the output isn't just useful, it's defensible.

What a Quoin Due Diligence Report Actually Looks Like

It's one thing to describe a research process. It's another to see it.

A Quoin due diligence report works through a fund or manager the way a thorough analyst would - pulling manager background, strategy analysis, performance context, fee structure benchmarking, legal structure, and regulatory history into a single structured output. Every section is sourced. Every claim is traceable back to the underlying document or data point it came from.

The result slots directly into an RIA's existing due diligence workflow without requiring custom formatting or cleanup. And because the sourcing standard is built into the system rather than dependent on individual effort, quality is consistent - regardless of who runs the report or under what time pressure.

See it for yourself: here's a live example of a Quoin due diligence report →

Compliance Doesn't End at the Investment Decision

Here's a dimension of the RIA due diligence problem that often gets underweighted: the research doesn't expire at closing.

The SEC has been increasingly explicit about its expectations around ongoing monitoring of alternative investments. IM Guidance on alternative investment due diligence establishes the foundational framework, and the SEC's 2023 examination priorities explicitly call out alternative investment oversight as an active examination focus. RIAs are expected to know what's happening with the managers and funds in their clients' portfolios - on a continuing basis, not just at the point of initial allocation.

For firms managing meaningful alternatives exposure across dozens of clients, that's a significant and largely manual burden.

Quoin's monitoring feature addresses this directly. Once a fund or manager is added to a portfolio, Quoin continues to track it - surfacing material developments, flagging changes relevant to the original investment thesis, and generating updated alerts that keep the advisory team informed without requiring new research to be run from scratch each quarter.

The compliance value is concrete: RIAs have documented, timestamped evidence that they are actively monitoring their clients' alternative holdings. That's not just good practice - it's increasingly what examiners expect to see. The Investment Adviser Association has consistently emphasized ongoing monitoring as a best practice cornerstone for advisers with alternatives exposure, and FINRA's guidance on AI in financial services reinforces the expectation that AI-assisted workflows meet the same oversight standards as traditional ones.

Built for the Way RIAs Actually Work

Most AI tools in financial services are being sold on speed. Quoin is built on accuracy and defensibility - which happen to be the two things that matter most to an RIA operating under fiduciary duty.

Agentic research that cites everything. Continuous monitoring that keeps portfolios current. A compliance record generated as a natural byproduct of normal workflow. For RIAs serious about alternatives - and serious about their obligations to clients and regulators - that combination represents something meaningfully different from the AI tools that came before it.

Learn more at quoin.ai, or explore a live due diligence report to see the research in action.

The Hallucination Problem Is a Fiduciary Problem

The first wave of AI tools introduced something the financial services industry wasn't prepared for: confident, fluent, entirely wrong answers. Hallucinations - where AI systems generate plausible-sounding information that has no basis in fact - aren't a theoretical edge case. They're a documented pattern, and the stakes in an advisory context are high.

The Financial Planning Association has explicitly flagged AI hallucinations as a compliance risk, identifying them as a source of inaccurate guidance that can expose advisory firms to liability under existing Advisers Act provisions. The SEC, while withdrawing its proposed "Predictive Data Analytics" rule in June 2025, was clear that existing fiduciary duties, supervision obligations, and anti-fraud provisions apply fully to AI-assisted activities. The absence of an AI-specific rule is not a license for unchecked automation - it's a call for self-imposed governance.

For an RIA, a hallucinated fact in a due diligence report isn't a minor inconvenience. It's a potential breach of fiduciary duty. That's exactly the problem Quoin was built to solve.

What "Agentic" Actually Means - and Why It Changes Everything

There's a lot of noise around the word "agentic" in AI right now. In Quoin's case, it has a specific and meaningful definition: rather than generating a response from a single prompt, Quoin's research engine autonomously plans, queries, cross-references, and synthesizes - breaking complex due diligence questions into discrete research tasks and executing them in sequence.

The practical result is research that shows its work.

Every claim in a Quoin report is tied to a source. Every figure is traceable. The system doesn't speculate - it cites. If a data point isn't substantiated, it isn't included. For RIAs who need to defend their process to clients, CCOs, or regulators, this distinction matters enormously.

This isn't a chatbot with guardrails bolted on after the fact. The citation-first architecture is foundational to how Quoin generates research - which means the output isn't just useful, it's defensible.

For a deeper look at how agentic AI differs from standard LLM approaches in an RIA context, read our full breakdown here →

What a Quoin Due Diligence Report Actually Looks Like

It's one thing to describe a research process. It's another to see it.

Quoin's own research into AI adoption among RIAs found that practitioners are in broad agreement on one point: AI's proven value today lies in operational efficiency and eliminating manual workflows - not in generating alpha or replacing investment judgment. Due diligence is precisely the kind of high-surface-area, source-intensive work where that efficiency matters most.

A Quoin due diligence report works through a fund or manager the way a thorough analyst would - pulling manager background, strategy analysis, performance context, fee structure benchmarking, legal structure, and regulatory history into a single structured output. Every section is sourced. Every claim is traceable back to the underlying document or data point it came from. The result slots directly into an RIA's existing workflow without requiring custom formatting or cleanup.

Because the sourcing standard is built into the system rather than dependent on individual effort, quality is consistent - regardless of who runs the report or under what time pressure.

See it for yourself: here's a live example of a Quoin due diligence report →

Compliance Doesn't End at the Investment Decision

Here's a dimension of the RIA due diligence problem that often gets underweighted: the research doesn't expire at closing.

The SEC's IM Guidance on alternative investment due diligence establishes the foundational framework for ongoing monitoring obligations, and the SEC's 2023 examination priorities explicitly call out alternative investment oversight as an active examination focus. RIAs are expected to know what's happening with the managers and funds in their clients' portfolios on a continuing basis - not just at the point of initial allocation.

For firms managing meaningful alternatives exposure across dozens of clients, that's a significant and largely manual burden.

What makes this harder is the regulatory moment we're in. The SEC withdrew its proposed Predictive Data Analytics rule in June 2025, but Morrison & Foerster's guidance to investment advisers is unambiguous: supervision obligations require written compliance policies addressing AI use, including accuracy verification and human oversight. The Investment Adviser Association has consistently emphasized ongoing monitoring as a best practice cornerstone for advisers with alternatives exposure. And FINRA's guidance on AI in financial services reinforces that AI-assisted workflows must meet the same oversight standards as traditional ones.

Quoin's monitoring feature addresses this directly. Once a fund or manager is added to a portfolio, Quoin continues to track it - surfacing material developments, flagging changes relevant to the original investment thesis, and generating updated alerts that keep the advisory team informed without requiring new research to be run from scratch each quarter.

The compliance value is concrete: RIAs have documented, timestamped evidence that they are actively monitoring their clients' alternative holdings. That's not just good practice - it's increasingly what examiners expect to see.

The 10% Problem

Return to that Schwab statistic: 63% of RIAs are using AI, but only about 10% have truly integrated it. The firms in that 10% share identifiable traits - a clear AI vision, investment in staff upskilling, strong data foundations, and a deliberate phased approach that starts with low-risk, high-ROI applications before moving to more complex ones.

Due diligence and ongoing monitoring sit at the intersection of the highest-stakes and highest-complexity AI applications for RIAs. Getting them right requires more than a capable language model. It requires an architecture built around accuracy, citation, and the kind of audit trail that holds up to scrutiny.

Most AI tools in financial services are being sold on speed. Quoin is built on accuracy and defensibility - which happen to be the two things that matter most to an RIA operating under fiduciary duty. The combination of hallucination-resistant agentic research and continuous portfolio monitoring means Quoin functions less like a productivity tool and more like institutional-grade research infrastructure - one that scales with the firm, maintains consistent quality standards, and generates a compliance record as a byproduct of normal workflow.

For the RIAs serious about closing the gap between experimentation and integration, that's a meaningful place to start.

Learn more at quoin.ai, or explore a live due diligence report to see the research in action.

Quoin's research on AI adoption among RIAs informed sections of this post. Read the full report: Five Proven Ways RIAs Can Deploy AI Today →