What is Broker Research and RMS Systems (And How to Actually Use Them)

Broker research, also called sell-side research, is the analytical reports, financial models, and investment recommendations produced by investment banks, broker-dealers, and independent research firms like Veritas Investment Research for institutional investors. A Research Management System (RMS) is the software that helps those investors organize, search, and act on that research. Together, they're the backbone of how institutional investment decisions actually get made.

If you've ever opened your inbox at 6 AM to find 47 new research reports from a dozen different banks, each with a different take on the same earnings print, you already know the problem. There's enormous value buried in broker research, but also enormous chaos. This guide covers what broker research is, how institutional investors consume it, what RMS platforms exist, and how AI is starting to change the whole workflow.

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How sell-side research flows from analysts to portfolio managers

Sell-side research is produced by analysts at firms like Goldman Sachs, Morgan Stanley, J.P. Morgan, Barclays, and hundreds of independent research providers. These analysts cover specific sectors or companies, building detailed financial models, publishing investment recommendations (buy, hold, sell), and setting 12-month price targets based on DCF, comparable company, and sum-of-parts valuation work.

The distribution chain is pretty straightforward. An analyst publishes a report, it passes through compliance review, and then gets pushed to clients through multiple channels: direct email from equity sales teams, the broker's proprietary portal, Bloomberg Terminal, FactSet, and LSEG Workspace. From there, equity sales professionals call their buy-side clients with 30-second pitches highlighting whatever matters most: an upgrade, a price target change, or a data point that shifts the thesis.

On the receiving end, buy-side analysts and portfolio managers at asset managers, hedge funds, and pension funds use this research to validate their own investment theses, discover new opportunities, benchmark their proprietary models, and gauge market consensus. A typical large buy-side firm receives research from 50 or more sell-side firms every morning. The reports are only part of the value. Firms also get access to management (through analyst-hosted conferences), industry data aggregation, and a range of analytical perspectives on the same name.

The seven types of broker research reports

Not all broker research is created equal. Understanding the different report types helps analysts prioritize what to read and when.

Initiation reports are the most comprehensive. They're essentially a full business case for why an analyst believes a stock is worth owning or avoiding. Flash notes are the most time-sensitive and can move markets within minutes of publication. Morning notes set the daily agenda for sales teams and clients.

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What is a Research Management System?

A Research Management System (RMS) is web-based software that pulls all investment research, both internal and external, into a single searchable platform. Analysts use it to create and share their work. Portfolio managers use it to act on that work. Compliance teams use it to track everything. If you want a simple analogy, it's the operating system that sits underneath the buy-side investment process.

What does an RMS actually do? It aggregates research from multiple broker sources, manages entitlements (controlling who on your team can access which broker's research), provides full-text search and discovery, supports annotation and note-taking, tracks compliance with audit trails, and integrates with your portfolio management and order management systems. The better systems also handle broker voting (ranking research providers by value), research budget management, and increasingly, AI-powered summarization and search.

RMS adoption picked up significantly after MiFID II took effect in January 2018. A survey by MackeyRMS found that 96% of buy-side firms without a dedicated RMS planned to adopt one within two years. That makes sense when you consider that investment research represents up to 30% of total operating costs for buy-side firms. Managing it well creates what the industry calls "operational alpha," which is edge gained not from better stock-picking, but from better process.

Live research vs. aggregated research: why it matters

Institutional investors access broker research through two very different models, and the distinction matters a lot when you're choosing tools.

Live (real-time) research arrives the moment it's published. Brokers deliver it directly via email, proprietary portals, and platforms like Bloomberg. You need a formal entitlement to receive it, which is basically permission from the broker, usually granted based on a trading relationship, advisory relationship, or coverage relationship. If you're running an event-driven strategy or actively trading around catalysts, live research is table stakes.

Aggregated or aftermarket research (AMR) is the same content, just delayed. The delay varies by broker and provider; there is no single standard window. Platforms like AllMind, AlphaSense's Wall Street Insights, and LSEG Workspace aggregate AMR through provider relationships. The upside is that you don't need individual broker entitlements. The downside is obvious: you're not the first to see it. AllMind sits on both sides of that split. Aftermarket research is included with no entitlement of your own, while live, embargoed research still runs through the firm's own RMS entitlement with the provider it already uses.

In practice, most sophisticated institutional investors use both. They keep live entitlements with their top 10-15 broker relationships for time-sensitive coverage and use aggregated platforms for broad historical search, cross-referencing, and sector-level research discovery.

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The major RMS platforms compared

The RMS market ranges from add-on features within data terminals to purpose-built standalone platforms. Here's how the major players compare.

Bloomberg Terminal

Bloomberg has the deepest data integration of any platform. Your internal research sits alongside market data, news, and analytics from 1,500+ research providers. The problem is cost: at a publicly reported $30,000 to $32,000 per seat per year, it's not cheap, and the RMS functionality feels like a bolt-on rather than a core product. The learning curve is steep, and the research management UX hasn't kept up with newer dedicated platforms.

AlphaSense

AlphaSense is probably the most recognized name in AI-powered research search right now. They hit $500 million in annual recurring revenue by October 2025 and serve 88% of the S&P 100. Their Smart Search uses NLP to understand what you're actually looking for rather than just matching keywords, which is a big deal in finance where the same concept shows up in a dozen different phrasings. The limitation is that AlphaSense is mainly an external content discovery tool. It doesn't give you the internal note-taking workflows, compliance tracking, or investment process management you'd get from a full RMS. Pricing runs from $10,000 to over $100,000 per year depending on team size.

Dedicated RMS platforms: VerityRMS, Bipsync, and CalibreRMS

VerityRMS and Bipsync are built from the ground up for internal research workflow. Verity targets fundamental investment teams with no-code configuration and vendor-agnostic integrations. Bipsync serves a wider audience including LP/GP allocators (15 of the top 20 US university endowments use it). Both have added AI features in recent years, though they came to it later than AlphaSense.

CalibreRMS is an Australian platform that fills a specific niche: ESG and stewardship. It handles proxy voting integration, engagement tracking, and SFDR reporting. If your firm needs ESG baked into the research process, Calibre covers ground that larger platforms mostly ignore.

BlackRock Aladdin

Aladdin is the dominant portfolio management and risk analytics operating system in the industry, used by firms managing over $21 trillion in assets. It pulls in research capabilities through its broader ecosystem including Preqin and eFront for alternative investments. But Aladdin is built for the largest institutional investors. Implementation takes months and pricing reflects enterprise scale. For most firms, Aladdin's real value is in portfolio construction and risk management, not research workflow.

The gap in the market

Here's the thing: no single legacy platform gives you great AI search, deep market data, proper research workflows, and compliance management in one package at a reasonable price. Bloomberg has the data but charges a premium and its RMS is limited. AlphaSense has the AI but no workflow management. Dedicated RMS platforms have great workflows but limited external content. This fragmentation is what's creating room for a new generation of AI-native platforms.

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How MiFID II reshaped the research landscape

The Markets in Financial Instruments Directive II (MiFID II) took effect on January 3, 2018 and changed how broker research gets paid for, distributed, and consumed. Before MiFID II, asset managers paid for research implicitly through "soft dollar" arrangements, where higher trading commissions bundled research costs with execution. The global estimated cost of this bundled research market was around $5 billion.

MiFID II classified broker research as a prohibited "inducement" unless explicitly paid for. That left asset managers with two choices: pay from their own profits (P&L model) or set up a Research Payment Account (RPA) funded by pre-agreed client charges with full budgeting, oversight, and disclosure requirements.

The fallout was real:

• Research budgets fell by 20-30% according to the FCA
• Average analyst coverage per company dropped from 9.1 to 8.0
• 334 small and mid-cap companies lost analyst coverage entirely
• Most asset managers just shifted to paying from their own P&L because RPAs turned out to be operationally painful
• Independent research providers, who were supposed to benefit from unbundling, didn't see any material gain

Both the UK and EU have since walked things back somewhat. The UK FCA introduced a third "joint payment" option in 2023 that allows bundled payments with guardrails. The EU extended research bundling exemptions to companies with market caps below EUR 10 billion. The regulatory picture keeps shifting, which makes robust research consumption tracking and compliance tooling that much more important.

For buy-side operations teams, MiFID II created a set of hard requirements: systematic research budgeting, formal quality assessments of research providers, entitlement management to prevent receipt of unsolicited research, and complete audit trails. These requirements have been a major driver of RMS adoption across Europe and, increasingly, worldwide.

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AI is changing how analysts consume research

Here's a stat that should bother anyone running a research team: analysts spend 70% of their time looking for and organizing information rather than actually analyzing it. A single portfolio manager at a typical firm gets research from 30+ brokers every day. A Federal Reserve research paper found that information overload in financial markets causes measurable underreaction. Put simply, investors can't process what they receive fast enough to act on it well.

AI is addressing this in four concrete ways.

1. Intelligent search and discovery

Keyword search has always been a poor fit for finance because the same concept gets described differently across reports. Semantic search powered by NLP (pioneered by AlphaSense's Smart Synonyms and now part of Bloomberg's Document Search, which covers 200 million+ company documents) understands what you mean rather than just matching words. If you search for "pricing power erosion," you'll find reports discussing "margin compression from competitive dynamics" even when those exact words don't appear.

2. Automated summarization and extraction

Generative AI can produce investment-grade summaries of earnings calls, broker reports, and regulatory filings in seconds. Schroders built an internal tool called GAiiA (Generative AI Investment Analyst) that generates draft investment memos with citations back to specific source documents. One portfolio manager said analysis that would have taken a junior analyst two weeks got done in minutes. According to a 2026 Brunswick Group survey, 46% of institutional investors now skip earnings calls entirely and just review AI-generated summaries. Even more telling: 40% trust AI summaries as much as those written by the sell-side.

3. Cross-document analysis

This is where things get really interesting. When AI can work across documents instead of just within them, you can do things that were practically impossible before. Tools like AlphaSense's Generative Grid let you run multiple prompts across many documents at once. You can track how 15 brokers changed their price targets on a single stock, or trace how management language about a specific business line shifted across four quarters of earnings calls. Try doing that manually across 60 PDFs.

4. Unified data integration

The bigger shift is from tools that handle one data type to platforms that work across filings, broker research, news, transcripts, and alternative data at the same time. This is where AI-native platforms like AllMind come in.

Instead of bouncing between Bloomberg for market data, AlphaSense for broker research, and EDGAR for filings, unified AI terminals bring all these sources into one place. Teams have started using platforms like AllMind to help process research at scale. AllMind spans 750M+ documents and a premium dataset estate licensed from 100+ providers and partners, including FactSet fundamentals and Revere supply-chain relationships, LSEG estimates, S&P/Capital IQ market and index data, MSCI data, CME and other exchange feeds, filings, broker research, Expert Insights and alternative data. That means the platform is not only an RMS or an AI layer over uploads; analysts can query structured data, research documents and their firm's own evidence in one workflow. We also connect to the RMS a firm already runs, including Verity RMS, FactSet RMS and BipSync, so adding AllMind does not have to mean retiring the research-management system.

Deloitte estimates that top investment banks could see front-office productivity gains of 27-35% by 2026 through AI adoption. McKinsey projects that institutional investors deploying AI effectively could see an ROI of more than tenfold across investment returns, operational efficiency, and risk management.

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Building an effective research workflow in 2026

Whether you're a junior analyst setting up your first research process or a CIO picking platforms for a 50-person investment team, the same principles apply. You want to spend less time finding and organizing information and more time actually thinking about it.

Start with your entitlement strategy

Figure out your top 10-15 broker relationships and maintain live entitlements for time-sensitive coverage. Use an aggregated platform for everything else. Manage entitlements centrally. Bloomberg and most dedicated RMS platforms offer team-level entitlement management tools that prevent compliance headaches with unsolicited research. Before any of that research is fed to an AI system, check what the licence permits; the buy-side guide to broker research licensing for AI walks through the contract terms.

Centralize everything in one system

The single biggest workflow improvement most firms can make is killing the "research archipelago," where reports are scattered across email inboxes, broker portals, shared drives, and Slack channels. A proper RMS or AI-powered research terminal should be the single source of truth. Every report, internal note, and financial model should live in one searchable location.

Layer AI on top, not instead of

AI is great at summarization, cross-referencing, and pattern detection. It does not replace an analyst's judgment on whether a thesis is actually right. Use AI to compress the time spent on information gathering, then put the freed-up hours into the analytical and creative work that generates alpha. Platforms like AllMind are built around this idea: AI-powered analysis across 30+PB of data monthly, with the investment professional still making the actual decisions.

Track everything for compliance

After MiFID II, every research interaction, consumption event, and broker payment needs an audit trail. Even firms not directly subject to MiFID II are adopting these practices as a matter of best practice. Your RMS should automatically log who accessed what research, when, and how it was used in the investment process.

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Frequently asked questions

What is the difference between buy-side and sell-side research?

Sell-side research is produced by investment banks and brokers for external distribution to clients. Buy-side research is produced internally by asset managers, hedge funds, and pension funds for their own investment decisions. Sell-side research goes out to many clients at once; buy-side research is proprietary.

How much does broker research cost?

After MiFID II, research is typically priced at $5,000-$500,000+ per year per broker relationship, depending on coverage scope, analyst access, and firm size. Research budgets across the industry fell 20-30% after unbundling, with the FCA finding average decreases of around 6% across surveyed firms.

Can small firms access broker research?

Yes. Many mid-tier and regional brokers offer "open access" research that doesn't require entitlements. Aggregated platforms give you access to aftermarket research from major banks without needing direct broker relationships. Some platforms also have individual or small-team pricing tiers.

What is a Research Payment Account (RPA)?

An RPA is a MiFID II-compliant mechanism where an asset manager sets an annual budget for research, funded by pre-agreed client charges or commission sharing arrangements. RPAs require formal budgeting, regular reassessment, and disclosure to clients.

How is AI changing equity research?

AI is automating summarization of earnings calls and reports, enabling semantic search across millions of documents, extracting key data points (price target changes, earnings revisions, sentiment shifts), and running cross-document analysis at speeds no human team can match. Fifty-four percent of institutional investors now say AI outputs are an important part of their research process.

What is the difference between live research and aftermarket research (AMR)?

Live research shows up in real-time directly from brokers and requires formal entitlements. Aftermarket research (AMR) is the same content on a delay that varies by broker and provider, aggregated by platforms like AlphaSense and LSEG Workspace. You don't need individual broker entitlements for AMR. Most institutional investors use both.

What is BlackRock Aladdin?

Aladdin is BlackRock's portfolio management and risk analytics operating system, used by firms managing over $21 trillion in assets. It handles portfolio construction, risk management, trading, and operations. It has some research-adjacent features, but it's primarily a portfolio and risk platform, not a dedicated research management system.

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The research management stack is converging

The lines between market data terminals, research management systems, and AI search tools are blurring. Bloomberg is adding AI-powered document search. AlphaSense is pushing into workflow management. Dedicated RMS providers are embedding generative AI. And a new wave of AI-native platforms is being built to unify what legacy tools kept in separate silos.

For institutional investors evaluating their research technology stack, the debate has moved on. Almost nobody is still asking "do we need an RMS?" since 96% of firms without one plan to adopt one within two years. The question now is whether to bolt AI onto existing tools, consolidate onto a comprehensive platform, or go with an AI-native terminal that was designed for this workflow from the start.

The firms that get research management right do more than save time. They see more of the data they're already paying for, hold onto institutional knowledge when analysts leave, maintain cleaner compliance trails, and make better investment decisions faster. When edge in this industry is measured in hours, that counts for a lot.

If you're looking at how to modernize your research workflow, Request Trial to see how AllMind unifies premium market and financial data, broker research, filings, Expert Insights, alternative data and firm-owned evidence in a single AI-powered terminal.