The Best AI Tools for Financial Advisors: 2026 Guide

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Summary: The best AI tools for financial advisors in 2026 are specialized, not generic. Jump, Zocks, and Zeplyn lead meeting documentation. Plan Builder in Income Lab and RightCapital’s Iris automate plan building. Penny in Income Lab answers strategic client questions on real plan data. Holistiplan and FP Alpha read tax documents. Catchlight scores prospects. The deciding factor is trust architecture. Ask who does the calculating, whether the advisor can verify any answer, and where the client’s data goes.

The T3/Inside Information 2026 Software Survey asked 2,906 advisors to rate, on a 10-point scale, how much they expect AI to impact the future of the profession across specific activities. Raising back-office efficiency scored 7.72. Servicing clients directly scored 4.37, and 23.15% of respondents scored it 0 or 1. That gap is the single most useful fact about advisor AI in 2026: advisors expect AI to change the work behind the scenes far more than the work in front of the client. Our reading of that gap is trust. Advisors want the hours back; they do not yet trust the output in front of a client.

This guide is organized by the job you need done rather than by vendor category, and it covers the part most best-of lists skip: how to evaluate whether an AI tool deserves access to client data at all.

The Five Jobs Advisors Are Hiring AI For

AI tools for advisors cluster around five jobs: documenting meetings, building and updating plans, answering client questions in the meeting, analyzing tax documents, and finding prospects. No single tool does all five well, and the strongest tool in one category is often absent from the others.

Organizing by job matters because the “AI” label has stopped being the main driver of advisor adoption. The technology behind the tool may matter to some for its novelty and glitz, but over the longer term professional advisors care more about improving their businesses, their workflows, and the lives of their clients. A notetaker, a plan-building agent, and a lead-scoring model share almost nothing architecturally or in terms of your workflow. The risks are different, the compliance questions are different, and the evaluation criteria are different. Pick the job first, then the tool.

Meeting Documentation: Jump, Zocks, and Zeplyn

The advisor-specific notetakers have won this category, and the category itself is no longer niche: in its first year as a T3/Inside Information survey category, AI notetaking reached 42.86% market penetration among advisory firms (T3/Inside Information 2026). Kitces Research on Advisor Productivity found that generic tools like Zoom’s AI Companion had the highest adoption but the lowest advisor satisfaction, while advisor-specific tools rated meaningfully higher (Kitces Research, “Best AI Notetakers For Financial Advisor Meetings,” Kitces.com, March 2025).

The reason is workflow. A generic tool gives you a transcript. An advisor-specific tool gives you structured notes, follow-up email drafts, task lists, and a CRM record formatted the way a compliance officer expects to find it. The same research found advisors who produce the most extensive plans adopted these tools at nearly four times the rate of those producing the narrowest and most targeted plans: the heaviest documentation loads get the most value.

Three tools lead the meeting documentation category: Jump, Zocks, and Zeplyn. There are of course many other meeting note and transcript tools, but these three made our list because they are built for advisor workflows rather than adapted from generic transcription, have strong user ratings in advisor technology surveys, and clearly address recording and privacy posture.

Jump is the market-share leader by many accounts, rated 8.55 by users in the T3/Inside Information 2026 survey, and has expanded into what it calls an “AI operating system for advisors”. Jump attributes its headline time savings, 5 to 15 hours per week on pre- and post-meeting work, to Meet, its core documentation product. The current lineup and pricing: Meet at $100 per advisor per month, with Onboard for intake automation and Grow for growth analytics as add-ons at $50 each (July 2026).

Zocks also has large market share and rates very highly in user satisfaction (8.35 in the T3 2026 survey). Zocks differentiates on privacy: “All client conversations are confidential, never recorded”. It builds notes, drafts client emails, fills intake and account-opening forms from meeting data, and syncs to CRMs including Wealthbox, Redtail, and Salesforce, and planning tools such as eMoney. Carson Group and Osaic are listed customers. Pricing is quote-based.

Zeplyn, the third meeting system on our list, was built specifically for financial conversations rather than adapted from generic transcription. It handles meeting prep, structured notes, task delegation, and follow-up drafts, is SOC 2 Type 2 certified, and also works recording-free.

Honorable mentions: Mili, the top-rated notetaker in the T3 2026 survey at 8.69 and strongest with the largest RIAs, plus GReminders, Fathom, and FinMate AI.

Notetaking is also popping up as a feature offered as part of a broader software suite. These tools don’t typically compete directly with stand-alone systems like Jump or Zocks. Instead, they are built to add capabilities to another part of the advisor tech stack and make advisor workflows there flow much easier. Income Lab, whose core product is client-centered financial planning software, with deep and unique coverage of retirement income planning and tax planning, also offers a Zoom plugin called Scribe. Scribe joins Zoom meetings, transcribes in real time, extracts financial planning data from the conversation, and auto-populates plan fields, with an on-screen feed showing what it captured during the call so that advisors can address any missing or mistaken information in real time. Scribe also delivers a meeting summary by email afterward, and its output goes to the advisor for review before anything is relied on. Plan conversations get documented where the plan lives. Conversations with advisors show that some even use multiple AI meeting agents at a time, such as Zocks and Income Lab’s Scribe, because they do different jobs. Scribe is one of six AI features included with Income Lab Pro.

Advisor takeaway: Choose a notetaker on satisfaction and workflow fit, plus fit with your compliance needs. Ask any vendor to show the full path from spoken conversation to compliant CRM record, and confirm whether recordings are stored, since that drives compliance review and client consent language.

Plan Building and Updating: From Documents to a Working Plan

Meeting agents exploded in use early in the commercialization of AI because they saved so much time and effort. They took basic tasks like taking notes and summarizing them and shifted this to an AI agent, freeing the advisor up to focus on the client.

The next, and perhaps even more impactful place AI is starting to make a difference is in building and maintaining financial plans. This is the job where AI saves the most hours per use. Turning a stack of PDFs into a populated financial plan used to consume an advisor or paraplanner’s afternoon or entire day. In 2026, only two planning platforms have entered this space.

Income Lab is the leader in this area, having launched its plan-building agents first, in late 2025, and covering more modalities, more stages of the client relationship, and more parts of client interaction than its competitors, who focus only on one tool and stage. Plan Builder in Income Lab turns documents into a plan. Upload a previously created financial plan PDF from RightCapital, MoneyGuidePro, eMoney, or NaviPlan. Upload a meeting transcript, meeting notes, a client intake questionnaire, Social Security statements, or anything else with useful information for building a plan, and Plan Builder creates a full financial plan in about 30 seconds, flagging any field it is uncertain about for advisor review.

The same workflow and technology appears in Income Lab’s Plan Updater, which keeps existing plans current: the Plan Updater detects new information in uploaded documents and transcripts and proposes changes that the advisor approves item by item. This capability means Income Lab’s AI agents don’t just handle initial plan creation, but ongoing plan updates as well. This is why, when it launched these capabilities in 2025, Income Lab claimed to have “solved data entry”. Used well, these tools mean the days of someone typing text into little white boxes on a screen are gone.

Another thing that stands out is that Income Lab realized that building plans isn’t just about taking in existing documents. Documents are sometimes the result of other interactions and other advisor-team work. So Income Lab built plan-building tools that jump into the flow where it happens, before or while those documents are being created:

• Live and virtual meetings. Income Lab’s Scribe Zoom plug-in builds plans during a client meeting. Not only does it listen and gather information, it gives advisors a running feed of identified plan items during the meeting so they can address missing or erroneous information while everyone is still on the call.
• Client intake before the meeting. To round out the plan-building suite, Income Lab also offers a client intake Interviewer, which gathers information from the client via chat, focusing on information needed to build a plan. Think of this like an automated client intake questionnaire that can be sent to the client via email before a meeting. The client does a 5 to 10 minute chat, in one sitting, and the advisor gets a draft plan.

With all of these tools, nothing enters the plan without advisor sign-off, so there are no surprises or automatic plan changes to confuse advisors or clients. Instead, the tools simply remove more than 90% of the painful work of data entry.

Income Lab’s four plan-building surfaces, Plan Builder, Plan Updater, Scribe, and Interviewer, are part of the six-feature AI suite included with Income Lab Pro. For a walkthrough of what this looks like in practice, see how advisors run Income Lab in client meetings.

RightCapital’s Iris, launched June 23, 2026, is the newest entrant to this space, likely created in part in response to Income Lab’s 2025 launch of its Plan Builder. Iris generates plan strategies (Plan Builder), reviews client profile data for gaps and inconsistencies (Double Check), and surfaces cash flow anomalies and planning gaps (Cash Flow Reviews). RightCapital states that all Iris outputs come from its own calculation engine rather than an outside source, which is the right architectural choice. Iris is included at no additional cost on RightCapital’s Premium ($209.95 per advisor per month) and Platinum ($254.95 per advisor per month) tiers (WealthManagement.com, June 23, 2026).

Iris is an example of how AI technology hit the industry in 2025 and 2026 and changed the vision of what software can do for advisors. In late 2025, RightCapital launched a specialized optical character recognition (OCR) tool that reads eMoney plans and creates RightCapital plans. That tool used old technology, since OCR has been around in some form or another for 100 years, and it was very limited: it only worked with select eMoney plan PDFs. It was designed for a narrow use case, helping advisors migrate from eMoney to RightCapital. It needed to know exactly what to expect, which is how OCR readers work, similar to Holistiplan’s tax return OCR reader, where the form 1040 is a known item, and it worked with only a small set of inputs.

This shows the real impact of AI: Income Lab’s AI Plan Builder, which was launched simultaneously with RightCapital’s OCR in 2025, works with completely unstructured data. There are no expectations that what an advisor uploads has a particular format. This means AI-enabled software now meets the advisor where they live, in their existing workflows. It doesn’t force the advisor to fit their work into the software’s requirements.

FP Alpha approaches the job from breadth: its AI reads tax returns, wills, trusts, and insurance policies and surfaces recommendations across 16 planning disciplines. FP Alpha generates recommendations from AI document reading; its calculation architecture is not publicly specified.

In-Meeting Client Questions: AI That Answers With Real Numbers

The least crowded and highest-stakes job is answering a client’s question during the meeting, with their actual numbers. This is the job advisors score lowest for expected AI impact in the T3 numbers, and the one where the architecture question matters most.

Here is the moment this category exists for. A married couple, age 64 and 62, asks in the annual review: “Can we actually afford $18,000 for the anniversary trip?” In Income Lab, the guardrails-based plan answers that from the plan itself, on screen, with a dollar figure while the couple is still asking. Then comes the follow-up: “If we convert $50,000 to Roth this year, what does that do to our Medicare premiums?” That question crosses tax brackets, the income-related monthly adjustment amount (IRMAA), and its two-year lookback on modified adjusted gross income (MAGI). When a client asked this question before, live, the advisor usually had to answer, “let me get back to you.”

Penny, Income Lab’s AI paraplanner, is built for exactly that follow-up. Penny works on the client’s real plan data inside Income Lab, not on plan settings rebuilt in a specialized planning tool. And while the tool is AI-supported, so advisors can interact with it using natural language chat, the calculations it presents come from the platform’s deterministic tax and planning engines, rather than from a language model doing arithmetic. Ask about the $50,000 conversion and the tax math, bracket effects, and IRMAA lookback implications come from the same engines the plan runs on. Each response carries a Verify button that shows how the answer was produced and which sources support it. Tax scenarios can be cross-checked against an external model used by the Congressional Budget Office. Penny analyzes and presents results for the advisor to consider, keyed to the plan; it does not edit plans. The advisor reviews, adjusts, and signs off. For IRMAA appeals following a life-changing event, Penny generates the filing instructions, and the advisor or client files the appeal. The full case for this architecture is in our guide to the AI paraplanner category, and the feature detail is on the page for Penny itself.

Penny is one of six AI features in Income Lab, and all six are included with Income Lab Pro at $2,990 per year ($249 per month paid annually). Together they cover the whole plan lifecycle: Interviewer walks a client through a guided questionnaire that builds their household before the first meeting, Plan Builder and Plan Updater create and maintain the plan, Scribe documents the meetings, Penny answers the strategic questions, and Assistant answers how-do-I software questions. That is the widest span of this guide’s five jobs from any single platform, and all six features share one architecture: deterministic engines compute, the AI interprets and explains, and client data is never used to train the models. The suite also comes attached to the category’s leading platform: Income Lab holds 9.08% market share in retirement distribution planning, roughly twice the share of the next platform in the category (T3/Inside Information 2026).

RightCapital’s Iris also lives in this neighborhood: its Double Check and Cash Flow Reviews run inside the advisor workflow, focused on reviewing plan inputs and flagging gaps rather than on open-ended strategic questions. Between Iris and Penny, in-workflow AI on live client data went from novelty to competitive baseline in a single quarter of 2026.

Advisor takeaway: For any AI that will speak in front of a client, ask one question first: who does the math? If a deterministic engine calculates and the AI explains, an error is traceable. If the language model itself generates the numbers, you are one hallucinated figure away from costly mistakes. Demand a verification mechanism you can click, not a promise.

Tax Analysis: Holistiplan, FP Alpha, Hazel, and Tax Lab

Tax document analysis is the most mature AI-adjacent category, and in the tax planning world Holistiplan dominates market share. According to the T3/Inside Information 2026 survey, it holds 38.92% market share in tax planning software with an 8.86 out of 10 satisfaction rating.

This job lives within the “tactical planning” software category. Here we find specialized tools, often not part of larger planning suites, that help advisors make fine-tuned planning decisions that are often more short-term-relevant. But several other players linked to other platforms, like Income Lab’s Penny and Altruist’s Hazel, are entering the tactical tax planning space and gaining market share.

Holistiplan built its lead on optical character recognition (OCR), which is still the engine behind its tax return scanner: upload a client’s return and it extracts the key figures in about 45 seconds, then compares the profile against a strategy library to flag planning opportunities and produce a client-ready report. Holistiplan shows that the key to advisor value and productivity isn’t always AI itself. Holistiplan is still not particularly AI-forward, but still owns this category. Holistiplan’s stated 2026 roadmap expands workflow automation and AI capabilities while committing that core tax calculations “will continue to rely on a deterministic, algebra-based calculation engine” (Holistiplan announcement via ACCESS Newswire, March 17, 2026). For scanning a full client base’s returns each spring, it is a strong, proven tool.

FP Alpha’s tax module covers similar extraction ground with its own AI reading layer and extends into multi-topic recommendations. Because FP Alpha reads tax returns, wills, trusts, and insurance policies, its findings span 16 planning disciplines rather than tax alone. FP Alpha generates recommendations from AI document reading; its calculation architecture is not publicly specified.

Altruist’s Hazel AI also plays in the tax analysis world, taking on the manual work of reviewing tax documents, identifying planning opportunities, and modeling strategies, then turning that analysis into something the advisor can review and present to the client. Altruist positions it against Holistiplan, on planning-relevant tax analysis produced from tax forms. It shares the constraint every specialized tax tool has, though: without access to a broader financial plan, findings rest on historical tax documents and manual advisor inputs.

With its launch of Penny, Income Lab now rates highly in the tax planning space as well. Penny lets advisors upload tax returns and other documents and immediately see a tax report with important tax observations and planning opportunities. It also allows deep scenario planning, marginal rate exploration, Roth conversion analysis, and a lot more. What sets Income Lab’s tactical tools apart is that they live with the client’s long-term plan, so there is no recreating plans inside a separate specialized tool.

Access to the financial plan is the key. Tax forms are always historical documents. In 2026 the most recent tax form anyone has is from 2025. That tells the advisor what last year looked like, which is helpful. But being able to run tax analysis not only on tax forms but also on this year, or a future year, is much more valuable. Multi-year decisions, like a conversion schedule timed against a client’s distribution plan, depend on the full plan: income timing, spending, portfolio, benefits claiming. A standalone tool cannot see any of that without recreating the plan inside it. This is where Tax Lab in Income Lab takes a different approach: the tax analysis is connected to the full financial plan, so a strategy is evaluated against everything else happening in the client’s retirement, not against last year’s return alone. Penny adds the conversational layer on top, analyzing an uploaded 1040 for a client, age 71, in the context of the plan that client already has.

One of the stand-outs here is Penny’s Withdrawal Optimizer, which in the product sits inside Penny as Plan a Withdrawal. It builds a recipe for a requested client withdrawal, weighing taxes, withholding, RMDs, tax brackets, charitable giving, and more, and the advisor executes the resulting plan. What makes it valuable is not just that it works through a genuinely complex problem, but that it does so with the context of the full financial plan. It knows not only about investment accounts, holdings and basis, but also about non-portfolio income and the full tax situation for the current year. Security-level output depends on holdings and basis data being present; without it, the tool works at the asset-class level and estimates gains. Penny, Tax Lab, and the Withdrawal Optimizer are all part of the AI suite and Tax Lab tooling included with Income Lab Pro.

For a deeper comparison of the category, see our tax planning software guide and the Holistiplan alternative breakdown.

Marketing and Prospecting: The Newest Category

AI prospecting tools promise to tell you which leads to call first. The category is younger than the rest covered here and the claims deserve more skepticism, but one tool has real institutional pedigree.

Catchlight, created in Fidelity Labs, scores leads by likelihood to convert using a model trained on more than 100,000 investor-advisor conversions, and enriches each prospect record with up to 2,000 data points on criteria like estimated assets, age, and income. For firms with steady lead flow, prioritization addresses a real problem: most firms work leads in the order they arrived.

The other half of this category is generic: advisors using ChatGPT, Claude, or Gemini to draft newsletters, LinkedIn posts, and client letters. That works, with two hard rules. Every draft is marketing material your compliance process must review before it goes out, and client data never gets pasted into a consumer chatbot. A general-purpose model with client personally identifiable information in the prompt is a data-handling incident, not a productivity win.

How to Evaluate AI You Would Trust With Client Data

Every tool above will demo well. The differences that matter are architectural. Four questions separate AI you can defend to a client, a compliance officer, or a regulator from AI you cannot.

Who does the math?

There are two architectures in advisor AI, and they are not equally safe. In the first, the large language model (LLM) generates the numbers itself. LLMs predict likely text; they do not compute math like a calculator. Applied to tax math, this produces what we call fuzzy math: answers that are formatted perfectly, sound authoritative, and are sometimes wrong in ways you cannot predict. In the second architecture, deterministic calculation engines, the same kind that have powered planning software for decades, do all computation, and the AI only interprets the question and explains the result. Two current examples of the second architecture: Income Lab built its entire suite on it, and RightCapital states that all Iris outputs come from its own calculation engine. When a vendor cannot clearly tell you which architecture they use, assume that AI is doing the math.

Can you verify any answer?

Auditability is what eliminates hallucination risk. The test: take any number the AI gives you and trace it to its source in one or two clicks. Penny’s Verify button exists for this reason, showing the calculation path and sources behind each response, with an external cross-check available for tax scenarios. Whatever tool you evaluate, run the test during the trial. If the vendor’s answer to “how do I know this is right?” is “our model is very accurate,” that is not an answer.

Where does the model learn from?

Ask what the AI consults when it answers: current tax rules and your client’s actual data retrieved at answer time, or patterns absorbed in training, which go stale the moment a threshold changes. Then ask the question in reverse: does anything your clients upload train the vendor’s models? The answer must be no, in writing. It’s important for advisors to do their own due diligence on AI tools: insist on enterprise-level security and non-training guarantees. Don’t use cheap or free AI chat bots for client work. Remember, if the software is free, you (and your clients!) are the product.

What happens when it does not know?

A trustworthy system says “I can’t determine that from the available data.” An untrustworthy one guesses fluently. In the trial, ask something the tool cannot know, such as an account you never entered, and watch what it does.

Put the vendor conversation on paper with these questions:

Question for the vendor	What a good answer sounds like
Who performs the calculations?	“A deterministic engine computes; the AI interprets and explains.”
Can I audit any specific answer?	“Yes, every response shows its calculation path and sources.”
Is client data used to train your models?	“No, contractually. Here is the clause.”
How long do you retain uploaded documents?	A specific number of days, with deletion on request.
Are outputs archived for books-and-records?	“Yes, here is how notes and outputs flow to your archiving system.”
Are conversations recorded, and with what consent?	A clear recording policy with client consent language you can adopt.
What does the AI do when data is missing?	“It says so and asks. It does not estimate silently.”

Advisor takeaway: The two questions that matter most are “who does the math?” and “can I verify it?” Every other feature is negotiable. An AI tool that computes deterministically and shows its work can be wrong occasionally and still be safe, because you will catch it. A tool that generates numbers and hides its reasoning is unsafe even when it is usually right.

Remember Who Is In Charge

Just because AI tools speak authoritatively, and just because the best ones use deterministic calculation engines instead of “next best guess” statistical modeling on text, doesn’t mean the advisor’s job has been offloaded.

The advisor should still play an active and careful role in evaluating answers. Fat fingering and edge-case hallucination can infect any AI system, even the best. So the advisor should not take AI software answers as the final step.

Think of it this way. Before AI, advisors frequently used web searches to find information about important topics like tax regulations. But no one ever took the first Google search link as the answer without supplying a little common sense or evaluation. AI doesn’t change that. Take all AI results as inputs to the client planning and advice process, not final outputs.

The Tools at a Glance

Tool	Job	How output is produced	Connected to a full financial plan?	Pricing (July 2026)
Jump	Meeting documentation, intake, growth analytics	LLM summarization of meetings, advisor-built workflows	No; syncs to CRM	$100/advisor/mo core; $50 add-ons
Zocks	Meeting notes, forms, emails, CRM sync	LLM extraction, no recordings stored	No; syncs to CRM and planning tools	Quote-based
Zeplyn	Meeting prep, notes, tasks	LLM summarization, recording-free option	No; syncs to CRM	Quote-based
RightCapital Iris	Plan review and strategy generation	Outputs from RightCapital’s calculation engine	Yes; runs inside RightCapital plans	Included with Premium ($209.95/advisor/mo) and Platinum ($254.95/advisor/mo)
Holistiplan	Tax return analysis	OCR extraction plus rules-based strategy library	No; standalone tax scan and related tools	Quote-based tiers
FP Alpha	Multi-discipline document analysis	AI document reading and generated recommendations	No; standalone analysis	All-In-One $1,995/yr plus credit-based module packs
Altruist Hazel	Tax document review and strategy modeling	AI document reading, advisor-reviewed output	No; standalone analysis	Quote-based
Catchlight	Lead scoring and enrichment	Predictive model trained on 100,000+ conversions	No; prospecting layer	Quote-based
Income Lab AI suite (six features incl. Penny)	Intake, plan building, updating, documentation, client Q&A	Deterministic engines compute; AI interprets, explains, and cites	Yes; every feature works on the client’s live plan	All six included with Income Lab Pro: $2,990/yr ($249/mo paid annually)

FAQ: AI Tools for Financial Advisors

What is the best AI tool for financial advisors?

It depends on the job. Jump, Zocks, and Zeplyn lead meeting documentation. For AI connected to actual financial plans, the choice is between planning platforms: Income Lab’s six-feature AI suite with Penny, or RightCapital’s Iris. Holistiplan leads tax return scanning but is seeing significant competition from systems like Altruist’s Hazel and Income Lab’s Penny. For a planning-centered practice, the best answer is AI that lives inside your planning software, computes deterministically, and lets you verify every number.

Will AI replace financial advisors?

No, and advisors do not expect it to. When the T3/Inside Information 2026 survey asked advisors to rate how AI will impact the future of the profession, raising back-office efficiency scored 7.72 out of 10 while servicing clients directly scored 4.37, with 23.15% of respondents scoring it 0 or 1. The tools winning adoption make the advisor faster and more thorough. The advisor still owns judgment, the relationship, and the advice.

Are AI notetakers compliant for advisory firms?

They can be, with diligence. Meeting notes and AI outputs may constitute books and records, so confirm how outputs flow to your archiving system. Confirm whether conversations are recorded and stored, since that drives client consent requirements; Zocks and Zeplyn both offer recording-free operation. And confirm in writing that client data does not train the vendor’s models.

How is AI inside planning software different from using ChatGPT?

A general-purpose chatbot has no access to your client’s plan and generates its own numbers, which makes it useful for drafting and dangerous both for math and for leakage of client personally identifiable information (PII). AI inside a planning platform works from the client’s real data and, in the right architecture, hands all computation to the platform’s calculation engines. It is also typically built on enterprise-ready AI systems that don’t train on client data or save your prompts. The difference shows up on the hardest question a client can ask: “What should we do, given everything you know about us?” Only software holding the whole plan can answer that one.

See It On Your Own Client Scenarios

Income Lab’s AI suite, including Penny, Plan Builder, Plan Updater, Scribe, Interviewer, and Assistant, is included with Income Lab Pro at $2,990 per year ($249 per month paid annually). Book a Walkthrough to see it answer your hardest client question on a real plan.

Sources

• Joel Bruckenstein and Bob Veres: T3/Inside Information 2026 Software Survey, March 2026
• Michael Kitces, Kitces Research on Advisor Productivity: Best AI Notetakers For Financial Advisor Meetings, Kitces.com, March 17, 2025
• WealthManagement.com: RightCapital Launches AI Agent Iris for Advisors, June 23, 2026
• RightCapital: pricing
• Jump: pricing and AI Operating System announcement, Business Wire, March 2026
• Zocks: zocks.io
• Zeplyn: zeplyn.ai
• Holistiplan: holistiplan.com and T3 market share announcement, ACCESS Newswire, March 17, 2026
• FP Alpha: fpalpha.com and pricing
• Catchlight: catchlight.ai
• Altruist: Hazel AI
• Income Lab product documentation: help.incomelaboratory.com

Jump, Zocks, Zeplyn, Mili, GReminders, Fathom, FinMate AI, RightCapital, MoneyGuide Pro, eMoney, NaviPlan, Holistiplan, FP Alpha, Altruist, Hazel, and Catchlight are trademarks of their respective owners. Competitor feature and pricing descriptions are based on publicly available information.