From research to compliance, AI platforms like LevelFields and Saifr are redefining how wealth firms operate and scale.
AI
June 26, 2025
Table of Contents
Artificial intelligence is rapidly reshaping how wealth management firms operate. From automating research and compliance to enhancing client prospecting and portfolio analysis, new AI platforms promise to boost efficiency and investment performance. Below we explore seven cutting-edge AI tools gaining traction among professional wealth managers, with a focus on their capabilities, real-world feedback, recent innovations, and suitability for firms serving high-net-worth (HNW) or institutional clients.
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LevelFields AI is an AI-powered research platform that scans millions of market events, earnings releases, regulatory filings, analyst actions, etc. to flag stocks poised for significant moves. It focuses on event-driven trading signals, identifying companies “about to go on a run” due to catalysts or emerging growth stories.
The platform offers over 25+ pre-built strategies (e.g. “Activist Investor buys”, “Dividend Increase”) and monitors 6,000+ stocks, including many micro to small-caps to uncover opportunities clients might otherwise miss. Advisors can customize real-time alerts and screen by sector, event type, or win-rate, enabling both quick trades and longer-term ideas.
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Early users report that LevelFields delivers on its promise of consistent event-driven returns, typically spotting 3-10% short-term moves and occasionally much larger gains. “Levelfields helped me catch a trade that jumped over 50%,” one Reddit user noted, while also highlighting the platform’s value for finding longer-term investments and educating users with its weekly market insights.
Importantly, advisors caution that it’s not a get-rich-quick machine but a research aid – “not a tool to automatically make you money like an ATM, rather it provides data and opportunities for you to think critically”. In online forums, some traders credit it with improving their results, whereas a few skeptics advice due diligence given the abundance of trading tools. Overall, the sentiment is that LevelFields can save significant time by alerting advisors to actionable events (often within minutes of news breaking), though success still depends on the user’s judgment in executing trades.
LevelFields has been expanding its offerings to provide more value for active investors. It introduced a premium “Level 2” membership with features like expert-curated alerts, 180-day news archives, up to 100 custom event alerts, and even one-on-one coaching for subscribers. This premium tier gives wealth managers early access to new event categories and weekly AI-driven market analysis to help anticipate broader trends.
The platform’s AI models continue to be refined, for example, integrating more nuanced options trading strategies and historical pattern analysis aimed at improving win rates across bull and bear markets. While primarily known in retail trading circles, LevelFields has begun making inroads with professional firms. In fact, some wealth managers now leverage LevelFields’ off-the-shelf event strategies to run dynamic, actively-managed portfolios that beat the S&P 500. This crossover into the advisory space was highlighted by Farther Finance (a tech-forward RIA), which publicly noted the potential of event-driven AI in boosting portfolio performance.
Sample Macrosynthesis on February 24, 2025:
TLDR
For advisors managing high-net-worth client portfolios, LevelFields can function as an “AI research analyst” embedded in the team. It excels at scanning market-moving events—like FDA approvals, changes in leadership and capital allocations, or activist campaigns—that often go unnoticed by overextended staff and traditional metrics-only screeners. This ensures wealth managers never miss opportunities from smaller-cap names or early-stage news triggers.
When clients call worried—say, about a stock dropping on news of a government investigation—the advisor isn’t caught off guard. With LevelFields, they already have context. The platform’s event analytics can show, for instance, that similar investigations historically lead to a 10% drawdown but typically recover within 3 months. This gives the advisor data to recommend a confident response: “This dip is typical and often results in a 10% gain from this point forward. It may be a buying opportunity.”
Moments like these help advisors maintain client confidence, reinforce their expertise, and shift panicked conversations into strategic ones.
Beyond reactive use cases, firms can also use LevelFields to proactively generate timely trade ideas—hedging after corporate scandals, rotating into sectors after strong earnings, or selling options on holdings with bullish catalysts. The ability to customize alerts by client mandate (e.g., income-focused, short-term aggressive) lets firms tailor event strategies to distinct portfolios.
This is especially valuable for boutique firms aiming to deliver more active management without expanding headcount. For teams offering hybrid or tactical strategies, LevelFields helps them “level the playing field” with larger institutional desks, delivering research-grade insights on demand.
That said, every AI alert should be reviewed through the firm’s own due diligence lens. But when used thoughtfully, LevelFields becomes a reliable source of high-probability signals—and a client communications advantage that goes far beyond performance.
More sophisticated wealth managers that utilize option strategies can delivery excess of 5% additional returns using the events from LevelFields on core holdings. Alerts can provide opportunities for selling covered calls when premiums are rich, selling puts when stocks are in freefall, and buying calls on dips to take advantage of short term price discrepencies. These strategies can add 5-10% extra alpha to portfolios - gains which result in higher client retention and greater client acquisition through viral marketing.
Pricing:
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Jump is an AI assistant tailored for financial advisors that automates meeting notes, task tracking, and compliance documentation. It integrates directly with advisors’ communication platforms like Zoom or RingCentral calls, email, CRM systems like Salesforce, Redtail, and others. During a client meeting (phone or video), Jump’s AI will transcribe the conversation and instantly distill it into organized notes, action items, and follow-up reminders. It can differentiate topics (e.g. retirement planning vs. tax query) and produce a structured summary with bullet points of key advice given and any promises made. Critically, it also cross-references compliance requirements: for instance, if an advisor makes a performance comment, Jump will flag that in the notes for review.
CRM updates happen automatically and Jump writes the meeting recap directly into the firm’s CRM and creates any to-do tasks (like “send risk profile questionnaire” or “prepare Roth conversion analysis”) identified in the discussion. Essentially, it’s as if an assistant listened to your client call and handled all the after-meeting paperwork within minutes. According to the company, using Jump can cut post-meeting admin work by 90%, turning what used to be 30-40 minutes of typing and logging into a couple minutes of review. Beyond note-taking, Jump also offers features like email drafting (writing personalized follow-up emails based on the meeting content) and agenda prep, where it scans prior communications and prepares a briefing before the next client call.
Another innovative capability in development is an “Ask Anything” search, advisors can query Jump for details from past meetings (“What did we discuss about college funding with the Smiths?”) and the AI will retrieve the exact notes where that topic was mentioned. This effectively creates a searchable knowledge base of all client interactions.
Among wealth management professionals, Jump has generated enthusiastic reviews. One CFP shared on Reddit: “My notes are better than they’ve ever been and I can spend more of the meeting focusing on the clients. Highly recommend”. This sentiment that Jump frees advisors to be more present in conversations instead of scribbling notes is widely echoed.
In industry surveys of AI note-takers, Jump consistently ranks at or near the top for advisor satisfaction. Not only does it capture dialogue accurately, but advisors value that it manages the “everything that follows” a meeting: creating tasks, updating CRM, and prompting compliance checks. A wealth manager at a multi-billion RIA noted that Jump brought their post-meeting processing time down from almost an hour to just 3-4 minutes, with 95% of the documentation done automatically. This kind of efficiency gain (often cited as saving ~2 hours per day across an advisor’s meetings) is a major draw. On the compliance side, firms like that Jump provides an audit trail of what was promised or discussed, which can be crucial in resolving disputes or meeting regulatory requirements.
A few users of early app versions mentioned some transcription errors or glitches (common with new tech), but the company has been rapidly improving reliability. Importantly, Jump is designed for advisors, so it understands industry lingo (e.g. it won’t mistake “401(k)” or “ESG” for random phrases) better than generic voice assistants. Overall, user feedback indicates high ROI in time saved and a positive impact on service quality.
Jump has seen significant momentum, including a major enterprise partnership with Osaic (formerly Advisor Group) announced in early 2025. Osaic, one of the largest wealth manager networks with over 11,000 financial advisors selected Jump as an official technology provider for its affiliates. Through this partnership, Jump’s AI assistant is being rolled out to thousands of advisors, a strong endorsement of its effectiveness. Osaic’s internal tests showed Jump delivered up to two hours of time savings per advisor per day, and greatly streamlined compliance recordkeeping. On the product side, Jump’s team is continually adding features. In addition to the “Ask Anything” search across past meetings (which leverages NLP to let advisors instantly recall client details from months or years back), Jump is exploring predictive analytics.
The platform can analyze patterns in an advisor’s meeting notes and, for example, alert the advisor if “Client X hasn’t discussed their insurance in over a year” or suggest topics to bring up based on client life events mentioned in passing. The AI is also being refined to summarize and tag legal and planning documents; one use case is having Jump read a lengthy estate plan PDF and extract the key actionable points for the advisor.
Jump can scan an advisor’s upcoming schedule and pre-compose meeting agendas or even provide a quick briefing (pulling data from the CRM and past notes) before the meeting starts. These enhancements move Jump closer to being a full-fledged AI assistant that preps you, takes notes during, and follows up after every client interaction. Given the competitive market for advisor tech, Jump’s rapid rollout of new capabilities is positioning it as a leader in AI workflow automation.
Jump was built specifically for wealth management, so its fit is very direct. Any advisory firm from a solo practitioner to a large enterprise can benefit from automating meeting documentation. For firms managing HNW clients, Jump can be a game-changer in terms of scalability: advisors can handle more client meetings per week when they aren’t bogged down with writing notes and logging tasks after each one. This is especially useful for high-touch firms where meetings are frequent and detailed.
Compliance departments also appreciate Jump, since it creates consistent records and reduces the chance of something slipping through the cracks (every recommendation and client query is captured). By integrating with CRM and portfolio management systems, Jump ensures that client data is always up to date without manual data entry errors. For multi-advisor firms, there’s an added benefit: standardized notes and tasks mean if a client’s primary advisor is out, a colleague can quickly get up to speed from Jump’s notes. Moreover, from an audit and risk management perspective, having AI-generated transcripts and summaries of all client communications is a solid safeguard. Some firms initially worry that clients might object to meetings being transcribed by AI; in practice, wealth managers report that once the benefits are explained (better follow-ups, no details forgotten), clients are comfortable and even impressed by the advisor’s tech-forward approach. High-net-worth clients often demand white-glove service Jump helps deliver that by ensuring nothing the client mentions falls through the cracks.
In summary, Jump is well-suited for advisory firms aiming to increase efficiency and consistency. It effectively offloads an advisor’s clerical work to AI, allowing human advisors to focus on analysis and relationship-building, which is exactly where they add the most value.
Pricing (Annual)
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Saifr is a compliance and risk management platform that uses AI to streamline the review of financial communications. It was incubated by Fidelity Labs and specifically addresses the compliance pain points of wealth management firms, ensuring that advertisements, social media posts, website content, and client communications all meet FINRA/SEC regulations.
Saifr’s tools (notably SaifrScan and SaifrReview) act like an intelligent compliance assistant: as marketing teams draft content, the AI scans the text (and even images or video) in real-time and flags any problematic language or visuals. For example, if a draft blog post says “our fund guarantees 10% returns,” Saifr will instantly flag “guarantees” as a compliance no-no and suggest alternate wording or the need for a disclosure.
It can detect a wide range of compliance issues promissory language, exaggerated or unbalanced claims, missing disclosures, improper use of testimonials, outdated content referencing old regulatory regimes, and more. In 2024, Saifr expanded its AI models to also catch things like comparison or ranking claims, performance statistics about investments, and references to “tax-free” income (all areas that have specific regulatory restrictions). Impressively, Saifr doesn’t just flag issues; it often provides an explanation of why something is flagged and even suggests compliant alternative phrasing or required disclaimers. This educates content creators and speeds up revision cycles. SaifrReview is a collaborative workflow tool where marketing and compliance officers can jointly view content and Saifr’s findings, then approve, reject, or edit accordingly.
The AI performs what used to be the first-line manual review and does it in seconds rather than days. For firms producing large volumes of communications, Saifr effectively triages the work: low-risk content can be cleared quickly, while higher-risk items are flagged for human compliance officers to scrutinize more closely. Saifr also integrates via API and add-ins (e.g. it can plug into Microsoft Office or content management systems), so users don’t have to leave their native content creation tools to run a compliance scan.
Compliance and marketing teams who have adopted Saifr report significant efficiency gains. Fidelity’s clearing and custody unit has promoted Saifr to the advisors in its network as a solution to “create more compliant marketing, faster.” By using AI to do the monotonous first pass, advisors and marketing staff can focus on higher-level messaging rather than nitpicking wording. In practice, firms say that what used to require multiple back-and-forth cycles with compliance can now often be resolved in one round: Saifr catches the obvious issues and suggests fixes upfront, so by the time a human compliance officer sees it, the content is largely clean. This not only speeds up the review process (one firm noted cutting approval times from a week to a day or two) but also makes compliance officers’ lives easier, they’re reviewing AI-filtered content with clear justifications for any red flags.
Saifr’s accuracy is reportedly high: it was trained on vast datasets of regulatory guidelines and past marketing materials. Users appreciate features like image scanning (e.g. if a social media post has an image with text overlay, Saifr can OCR it and flag if you put a client testimonial quote without proper disclosure). Another piece of feedback is that Saifr helps educate advisors and marketing writers on compliance.
By seeing instant feedback as they type (for instance, a pop-up “This sentence may be considered promissory per FINRA Rule 2210”), people learn to write in a compliant way from the start, reducing revisions. We don’t see traditional “star ratings” for Saifr since it’s enterprise software, but anecdotal reception is positive especially among large broker-dealers that have to review thousands of advisor communications.
A Fidelity executive noted that regulations and content channels keep evolving, and AI tools like Saifr are essential to handle the growing volume of material without ballooning compliance headcount. One common question is whether AI can truly catch everything, compliance officers still do a final review, but they’ve grown to trust Saifr as a reliable filter that rarely misses obvious violations.
Since its initial launch in 2022, Saifr has continuously enhanced its AI models in response to new regulations and user needs. In May 2024, it rolled out an update that significantly broadened its detection capabilities as mentioned, it can now recognize comparative claims, specific performance figures, and unsubstantiated ratings in text. This came just in time as regulators have been zeroing in on use of testimonials and performance marketing in the wake of the SEC’s updated Marketing Rule.
Saifr also added a readability analysis feature: it uses AI to gauge the reading grade level of content and whether it’s appropriate for the intended audience (useful since overly complex language in client communications can be a risk). Another advancement is deeper integration with Microsoft: Saifr’s AI models became available in the Microsoft Azure Marketplace in 2023. This move means that large financial institutions using Azure can deploy Saifr’s compliance-checking AI within their own infrastructure easily, a nod to firms that prefer not to send data to third-party clouds. In terms of user interface, Saifr has been refining its collaborative dashboard.
The latest SaifrReview interface allows multiple reviewers to comment and track changes on content, much like Google Docs, but with compliance context for each comment. It also introduced role-based views, for example, a marketing user might see Saifr’s suggestions on how to make the content more engaging yet compliant, whereas a compliance officer’s view emphasizes rule citations and approval checklists.
On the horizon, Saifr is exploring AI for regulatory change monitoring: the idea is that the system will automatically update its flags as rules change and even notify the firm if existing published content (like an old blog post) has become non-compliant due to new regulations. Overall, Saifr’s recent innovations aim to keep it at the forefront of RegTech, ensuring that wealth management firms can confidently scale their marketing in a controlled, automated way.
Compliance is a universal challenge in wealth management, and Saifr offers a modern solution particularly well-suited for firms that produce a lot of client-facing content. Think of large RIAs and broker-dealers with many advisors, each sending client newsletters, running social media accounts, and hosting seminars, the volume of material can be immense.
Saifr allows these firms to maintain strict compliance standards without bottlenecking their marketing efforts. High-net-worth and institutional clients often receive bespoke reports or thought leadership pieces; Saifr can vet those quickly so the firm can communicate more frequently and freely with clients (which HNW clients appreciate) while staying within regulatory guardrails. Smaller firms benefit too: a mid-sized wealth manager might not have a full-time compliance reviewer for communications, Saifr can act as a virtual compliance officer that ensures the firm’s website updates or LinkedIn posts won’t get them in trouble. By catching issues early, Saifr reduces the risk of regulatory fines or reputational damage from an off-message post.
It’s also a tool that can bridge the gap between compliance and advisors: rather than compliance being seen as the “Department of No,” advisors get proactive guidance from the AI on how to shape their message in a compliant way, which makes the whole process more collaborative. Firms managing ultra-HNW money often rely heavily on reputation and trust demonstrating that they use advanced compliance technology like Saifr can be a selling point (assuring clients that the firm’s communications and products are thoroughly vetted by AI for accuracy and fairness).
In summary, Saifr is a strong fit for any wealth management firm that wants to market smarter and faster. It brings much-needed automation to compliance workflows, which in turn frees up both advisors and compliance officers to focus on higher-value activities (like crafting strategy or reviewing truly complex cases). Given that regulators are encouraging the adoption of compliance technology, implementing Saifr can also signal to regulators during exams that the firm is forward-thinking in managing its compliance risks.
Saifr Pricing:
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Catchlight is an organic growth platform that helps wealth management firms prioritize and personalize their prospect outreach using AI. In essence, Catchlight acts as a lead intelligence engine: advisors input their list of prospects (e.g. leads from marketing campaigns, referrals, or custodian programs), and Catchlight automatically enriches each lead with up to 2,000 data points from public and proprietary sources.
These data points include estimated investable assets, income range, age bracket, career info, financial interests (like whether they’re interested in ESG or crypto), even personal hobbies or philanthropic involvement. Using this rich profile, Catchlight’s machine learning model then assigns each prospect a “conversion likelihood score” essentially predicting which leads are most likely to become clients. The platform’s dashboard will highlight top-scoring leads so advisors know whom to call first. But beyond scoring, Catchlight aids in personalization: it suggests tailored talking points or engagement strategies for each prospect based on their profile (for example, if a lead’s data shows a passion for golf and an upcoming retirement, an advisor might invite them to a golf event and discuss retirement income planning). By having insights like a prospect’s approximate liquid assets, an advisor can also gauge which services or account types to pitch.
Catchlight essentially surfaces the “story” behind each lead, so advisors aren’t going in cold. Another key feature launched in late 2024 is AI-driven lead routing for firms with multiple advisors. This uses a proprietary model to match each prospect to the advisor in the firm who is statistically the best fit based on that advisor’s experience and their existing client demographics. For example, if a prospect is a 40-year-old business owner with moderate assets, the system might route them to the advisor who has a track record with Gen X entrepreneurs, rather than an advisor who primarily serves retirees.
The goal is to increase conversion rates by aligning prospects with the most suitable advisor or team. This kind of intelligent lead assignment can be hugely valuable in large practices or enterprise wealth managers. Catchlight also integrates with CRM systems (like Wealthbox, Redtail, Salesforce) and marketing tools (FMG Suite, for instance), so it fits into the advisor’s workflow, scores and data can appear right alongside each lead in the CRM.
Advisors using Catchlight have reported impressive improvements in prospecting efficiency. One financial advisor, Tim Gardner, shared that after adopting Catchlight, he cut his prospecting time by 30% while tripling his rate of booking meetings with qualified leads. By having richer intel on each prospect, he could focus his energy on the ones most likely to say yes, and approach them with more relevant conversations leading to far more meetings converting to clients.
Another large success story is Advisors Excel (AE), a nationwide advisor network/annuity platform, which added Catchlight for its 500+ affiliated advisors. AE’s Chief Strategy Officer praised how Catchlight accelerates and scales the firm’s ability to learn about leads “understand their financial goals, and speak directly to those needs in a way we could never have imagined even a few years ago,” he said. Advisors Excel noted that in a world where prospects are bombarded by generic advisor solicitations, the personalized approach enabled by Catchlight helped their advisors stand out and engage prospects more authentically.
Indeed, Catchlight doesn’t just score leads it often suggests which aspects of a lead’s profile to mention. For instance, if the AI knows a prospect recently sold a house (public records) and has cash, it might nudge the advisor to discuss investing that cash. Advisors find this guidance extremely useful in initiating contact. In terms of quantitative feedback, Catchlight won a WealthManagement.com “Industry Awards” honor in 2024 for Best Prospecting Innovation, reflecting strong industry reception.
The platform’s ease of use is often cited advisors can upload a simple CSV of leads and within minutes get a prioritized list with scores and profiles. Some feedback for improvement has been around data accuracy: occasionally the estimated assets or income might be off for a given individual, since the AI is making probabilistic guesses. However, the general ranking of hot vs. cold leads is reported to be highly predictive.
Firms appreciate that no prospect is discarded; even lower-scoring leads still get some insights and can be nurtured differently. Overall, the feedback is that Catchlight helps advisors focus on the right people with the right message, which is the Holy Grail of prospecting.
Catchlight has been rapidly evolving its feature set. The big late-2024 rollout was the “Lead Routing” AI model in beta. This innovation tackles the challenge of matching prospects to advisors in larger firms: by analyzing an advisor’s existing successful clients, the AI finds prospects with similar attributes and suggests pairing them up. Early trials indicated this boosts conversion because, for example, an advisor who excels with high-net-worth doctors will likely resonate better with a doctor prospect than an advisor who mostly handles small business owners.
In 2025, we can expect this feature to become generally available and more automated (potentially auto-assigning leads to advisors in a CRM queue). Another 2024 enhancement was deeper CRM integration, Catchlight released a Salesforce app and improved integrations such that lead scores update dynamically and trigger workflows (e.g., if a lead’s score jumps because new data came in, the advisor is alerted).
The platform is also incorporating more real-time data feeds. Initially, a lot of data points were relatively static (from public records, etc.), but now Catchlight is pulling in dynamic signals for instance, monitoring if a prospect changes jobs (via LinkedIn or other data) or if there are news mentions of a liquidity event. This allows the AI to adjust a prospect’s score if their situation changes (say a prospect suddenly comes into money by selling a business, they’d become a higher priority). On the analytics side, Catchlight introduced a funnel analysis tool that helps firms see where leads are dropping off in the pipeline and how high-score leads compare to low-score leads in conversion, providing feedback to marketing.
They also launched an Advisor Growth Program with educational modules, since many advisors need guidance on digital marketing and follow-up, the AI can give you leads, but you still have to close them. By combining training with tech, Catchlight aims to improve adoption and success rates. Lastly, Catchlight’s recognition has grown: it secured several partnerships (Advisors Excel in 2023, and in 2022 it emerged from Fidelity’s incubator and won the T3 Technology Innovation award).
It’s likely we’ll see Catchlight forming data partnerships to get even more granular prospect info (e.g., integrating credit bureau data or consumer spending data in a privacy-compliant way). This continuous innovation keeps Catchlight at the forefront of AI prospecting tools.
Catchlight’s value proposition is directly tied to growing AUM and client base, which is a priority for most wealth management firms. It is especially useful for firms targeting high-net-worth individuals, where finding the right prospects can make a huge difference. HNW prospects can be needle-in-haystack, Catchlight helps identify which leads in a mass list likely have significant assets (via its estimates), allowing firms to allocate their business development efforts efficiently. For instance, an RIA might have a thousand leads from a seminar, Catchlight can reveal which 50 have the highest investable asset potential and thus merit a personal call from a senior advisor, versus which are smaller fish to put into an automated nurture email campaign.
The personalization insights are also critical for HNW prospects, who expect a bespoke approach. If you know a prospect is a C-suite executive nearing retirement, you can approach the conversation very differently (and more relevantly) than if you knew nothing about them. Catchlight effectively gives even small or mid-sized firms the kind of data on prospects that only big banks or wirehouses used to compile (and even those often didn’t have AI scoring). For larger enterprises or advisor networks, Catchlight’s lead routing and scoring can significantly increase the ROI on leads, marketing dollars go further when leads are handled by the best-matched advisor with the right message.
It’s also a morale boost for advisors: cold prospecting is tough, but having an AI copilot that tells you “try these five people first, and here’s what to say” makes the process more efficient and successful. One could argue that very high-end boutique firms that rely purely on referrals might need Catchlight less; however, even referrals can be enhanced by additional intel (an advisor could run a referred name through Catchlight to learn about them before the first call).
With the wealth management industry seeing an increasingly competitive fight for new clients (and aging client bases prompting a need to attract younger wealth accumulators), tools like Catchlight offer a significant competitive edge. It allows firms to scale up their prospecting without blindly increasing headcount, by making each advisor or sales team member far more effective in turning leads into clients.
In summary, Catchlight fits any growth-oriented advisory firm, and it aligns particularly well with the strategic goal of boosting organic growth that many wealth managers have been tasked with.
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Kensho, a division of S&P Global, offers a suite of AI-powered analytics tools widely used in the financial industry including by wealth management analysts and portfolio managers. Kensho’s solutions are designed to unlock insights from the vast amounts of financial data and text that professionals deal with. Some of the flagship capabilities: Kensho Scribe provides on-demand transcription of financial audio, like earnings calls, investor days, or even client meetings, with extremely high accuracy and speed. This allows advisors to quickly get transcripts of, say, a Fed speech or a company’s quarterly call and keyword-search it or analyze sentiment.
Kensho NERD (Named Entity Recognition & Disambiguation) can take unstructured text (news articles, filings) and identify the companies, people, and topics mentioned linking them to S&P’s database. This helps wealth managers efficiently digest news by automatically tagging relevant tickers or clients. Kensho Extract uses natural language processing to pull structured data out of documents for example, extracting all the financial metrics from a PDF report or pulling key deal terms out of a press release. Instead of an analyst manually reading and highlighting, the AI does it in seconds. Kensho Classify can categorize documents or news by topic, which can assist in filtering information (e.g., finding all “inflation-related” research notes across thousands of documents). Perhaps the most exciting development is Kensho’s foray into generative AI assistants: S&P Global has integrated Kensho’s AI into an assistant called ChatIQ on their Capital IQ Pro platform.
ChatIQ (built in partnership with Kensho) allows users to ask natural language questions against S&P’s extensive datasets and knowledge base. An analyst or advisor can type a query like, “Show me the 5-year CAGR of revenue for the top 3 holdings in my portfolio” and ChatIQ will fetch the data from S&P’s databases and provide a direct answer or table. This dramatically speeds up research making what used to be a multi-step process of pulling data and calculating, into a single query.
Moreover, Kensho launched a LLM-ready API in late 2024 that lets financial institutions plug S&P’s data directly into their own AI models. This means a wealth management firm could integrate an AI chatbot on their internal systems that, through Kensho’s API, has access to S&P’s trove of market and company data (financial statements, market prices, M&A data, etc.) and can answer questions or run analyses in a conversational manner. Another Kensho capability worth noting is Kensho Link, which can map entities in the firm’s internal data to standard identifiers (like linking a client’s portfolio holdings to the official S&P company IDs) a behind-the-scenes AI that ensures data consistency and opens the door for more automation.
Taken together, Kensho’s tools function as an AI-enhanced research assistant, data miner, and analytical engine for finance.
Kensho’s technology has been highly regarded in the financial community for years (S&P acquired Kensho in 2018, recognizing its potential). For wealth management professionals, the benefits are often described in terms of time savings and deeper insight. For example, before tools like ChatIQ, an analyst might spend an hour gathering data from various sources to answer a client’s question about portfolio exposure or historical performance of a sector. Now, they can simply ask the AI and get an answer in seconds, freeing them to focus on higher-value analysis or client advisories.
Early users of ChatIQ note that it’s trained on S&P’s high-quality data and tailored to finance, which makes its responses far more reliable and specific than a general chatbot. One portfolio manager said it felt like having “a junior analyst on call 24/7 who can instantly reference any data you need.” The transcription service Scribe is praised for its accuracy; wealth firms use it to get quick summaries of earnings calls “I can search a transcript for the word ‘guidance’ immediately after the call ends, instead of waiting for transcripts the next day,” noted one investment strategist.
Kensho’s internal impact at S&P is telling as well, S&P’s own 40,000+ employees have been using an internal generative AI tool called Spark Assist, built by Kensho, to query internal data and draft reports. This has been cited by S&P’s leadership as significantly improving productivity and collaboration (a hint that external clients could see similar gains). Another piece of feedback: Kensho’s AI benchmarks and indices (like the S&P Kensho New Economies indices tracking innovative sectors) have given wealth managers new thematic tools to use with clients, though that’s more of a product angle.
On the analytics side, one chief investment officer at a wealth firm mentioned that Kensho’s tools helped his team rapidly analyze *“what-if” scenarios. By asking the LLM-ready API questions like “which of our portfolios have highest exposure to companies with declining profit margins?” they identified at-risk client accounts and reallocated before those stocks underperformed.
In essence, feedback highlights better decision-making with less grunt work. Advisors and researchers trust Kensho’s outputs because they tie back to verified S&P data. If there’s any caution, it’s the usual one with AI: understanding the limitations. Kensho’s generative answers are only as good as the data, the AI might not have the latest niche info or could occasionally misinterpret an ambiguous query. But users find that rephrasing questions or drilling down fixes that, and the overall accuracy is high for well-formed questions.
The bottom line from user experience is that Kensho’s AI tools allow wealth management professionals to operate at a higher level of insight in a shorter time, which is incredibly valuable in a fast-moving market environment.
Kensho and S&P Global have been very active in rolling out new AI-driven features. In 2024, S&P integrated Spark Assist, a generative AI co-pilot, internally and started piloting it for client-facing use. Spark Assist can draft report outlines, summarize lengthy documents, and answer complex queries by leveraging Kensho’s NLP and S&P’s datasets. Meanwhile, ChatIQ was introduced to S&P Capital IQ Pro users in mid-2024, giving buy-side and sell-side analysts a powerful research assistant. By late 2024, S&P reported that thousands of client queries had been handled by ChatIQ, indicating rapid adoption.
Kensho also launched AI Benchmarks, a set of standardized tests to evaluate AI models (like how well various large language models handle financial QA). This might seem academic, but it guides how they fine-tune their tools for the finance domain. On the product side, Kensho released improvements to Scribe (like a new feature to extract key quotes or topics from transcripts automatically, which helps advisors quickly find salient points from, say, a Fed meeting transcript). They also enhanced Kensho Classify and Kensho Extract to work in multiple languages, anticipating global use cases.
One headline in late 2024 was S&P Global opening up Kensho’s LLM-Ready API for beta testing with select clients. This is crucial: it means external developers (including fintech teams at wealth firms) can integrate S&P’s data with their own AI applications seamlessly, no need to manually clean or prep the data for the AI, Kensho’s API handles that. As a result, we’re seeing some wealth managers building custom internal chatbots for their advisors that, via Kensho, know all the market data and even internal research. Looking forward into 2025, S&P’s AI leadership has talked about “agentic AI” , essentially AI agents that can perform multi-step research tasks autonomously (for example, automatically finding data, running calculations, and producing a report draft). We can expect Kensho to be central in developing those capabilities for finance.
Additionally, S&P Kensho indices which cover themes like AI, space, drones, etc. have gotten updates to methodology and new launches, giving wealth managers more tools to invest in or benchmark against the “new economy” trends. All these innovations show Kensho pushing the envelope of how AI can be applied in wealth and investment management moving from just retrieving information to actually assisting in analysis and decision workflows.
Kensho’s AI tools are somewhat behind-the-scenes, often embedded in larger platforms, but they are extremely relevant for wealth management organizations that do a lot of research, reporting, or data analysis. For large private banks or investment offices, Kensho can turbocharge the productivity of research analysts, due diligence teams, and portfolio managers. Tasks like writing quarterly market outlooks, analyzing the impact of economic events on portfolios, or responding to client inquiries with data, all can be accelerated with Kensho’s generative AI and data automation. Even for financial advisors at smaller firms, if they have access to S&P Capital IQ or other S&P platforms, they can leverage ChatIQ to get quick answers (for example, “What’s the dividend yield of each stock in Client A’s portfolio and how does it compare to last year?” a query that might take hours manually, but moments with AI).
Kensho’s role in compliance shouldn’t be overlooked either: tools like Kensho Scribe can be used to document meetings (similar to but not as specialized as Jump) or to review communications for mention of certain entities (assisting compliance like Saifr, though in a different way). Essentially, Kensho provides institutional-grade AI infrastructure that wealth firms can plug into their processes.
High-net-worth clients benefit indirectly as they get more timely research from their advisors, more thorough answers (because the advisor could ask the AI a very detailed question they wouldn’t have tried to do manually), and potentially more innovative investment opportunities (like the Kensho thematic indices which can be used to craft unique portfolio tilts toward cutting-edge sectors). In competitive terms, a wealth manager using Kensho’s capabilities might be able to present insights or analysis to a client faster than a competitor who relies on traditional methods. For example, right in a client meeting, an advisor could use an AI assistant to answer an unexpected question about a stock or sector, impressing the client with immediate insight.
Firms do need to ensure their staff is trained to use these tools effectively and remains critical of outputs (AI isn’t infallible), but Kensho provides a lot of transparency by linking answers to source data. In conclusion, Kensho’s AI solutions fit any wealth management firm that places value on data-driven advice and efficient research. As the industry moves towards augmenting human expertise with AI, Kensho (backed by S&P Global’s data) is likely to be a cornerstone in the tech stack of modern advisory firms. It allows even modest-sized firms to leverage the kind of AI analysis that was once the domain of only the largest banks and hedge funds, thereby democratizing some of the analytical firepower across the wealth management landscape.
Kensho Pricing:
The wealth management industry is embracing AI not as a gimmick, but as a genuine booster to productivity and decision quality. Tools like LevelFields bring hedge-fund-like event trading insights to everyday advisors, PortfolioPilot democratizes institutional portfolio analytics, Jump automates the drudgery of notetaking and compliance, Saifr ensures marketing efforts stay compliant at scale, Catchlight turbocharges client acquisition, Axyon injects sophisticated quantitative power into portfolio management, and Kensho empowers data-driven insights on demand. Each of these platforms addresses a different facet of a wealth manager’s workflow from front-office client interactions to back-office compliance to investment selection.
Real-world feedback shows that when implemented thoughtfully, these AI tools can save significant time, uncover new opportunities, and ultimately help advisors deliver better outcomes and experiences for their HNW and institutional clients. Firms that successfully integrate AI into their operations stand to gain a competitive edge, operating more efficiently while enhancing the personal, human advice that remains at the core of wealth management. As we move into 2025 and beyond, the synergy of human expertise and artificial intelligence will likely become the norm among high-performing wealth management teams.
The tools profiled here are at the forefront of that transformation, enabling advisors to spend less time on routine tasks and more time on what matters, understanding clients and guiding them to financial success.
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