Equity research data platforms cut filing work

An equity research data platform should do more than store documents. It should help us move from a filing to a verified answer, then into a comparable financial view and a reusable output. That is the workflow we focus on when researching US and Nordic public companies.

What equity research data platforms need to do well

The basic job is simple to describe and hard to execute: find the right source, tie every number back to that source, and let us compare companies in a consistent format. In our own workflow, that means annual reports, interim reports, and SEC filings in one place, plus company pages, side-by-side comparisons, and source-linked answers. Our coverage includes more than 7,000 public companies in the United States, Finland, and Sweden.1

For analysts, the difference is not just speed. It is defensibility. If we can open the report page behind every figure, we can check the context before the number goes into a model or a note. That matters whether we are building a new comp set, checking management language against reported results, or preparing for an earnings review.

Search filings first, then verify every number against the source

In practice, the fastest workflow starts with filing search, not with a broad web query. We ask a plain-language question, search across annual reports, interim reports, and SEC filings, then follow up in the same conversation. Each answer is tied to a report page, which keeps the source visible while we work.1

That source traceability matters when a number looks off. Instead of copying a figure into a spreadsheet and hoping it matches later, we can open the underlying page, check the exact wording, and decide whether the figure belongs in the model. Our workflow is built around that check step: answer, source, verify.

For US issuers, we can move from a filing query to the exact EDGAR page. For Nordic issuers, we can work across Finnish, Swedish, and English reports from the same question, which saves time when the peer set spans markets and reporting languages.1

We also use this approach inside AI assistants through MCP. The same filings dataset can be queried in ChatGPT, Claude, Cursor, or another MCP client, with each read tool returning page IDs for audit-grade citations.2 If you want the integration details, see our MCP docs.

Build a comparable view across US and Nordic public companies

Analysts rarely need only one company. We usually need a peer set, a trend, and a clean way to compare periods. That is why a useful equity research data platform needs a consistent financial model across markets. In our company pages, income statement, balance sheet, and cash flow are shown in one format, with key metrics for 10+ years for US companies and 5+ years for most Nordic companies.1

This matters when we compare a US software company with a Nordic industrial or telecom name. We can use the same data model, the same period logic, and the same reporting structure instead of reconciling spreadsheets from scratch. Our comparison view places companies and periods side by side in one table, and our screening tools help us narrow a universe by financial metrics and multi-year trends.1

Company pages are useful when we need more than the headline numbers. They bring together the business profile, financial statements, filings, segments, and revenue by geography in one place, with links back to the original reports.1

That is the practical benefit for workflow research. We spend less time normalizing data and more time asking questions such as: which peer is growing faster, which one has the cleaner margin bridge, and which segment is driving the change. The platform does not replace judgment. It reduces the friction between a question and a defensible comparison.

Use AI inside ChatGPT and Claude with MCP for filing-based research

AI only helps when it stays connected to the source. Through MCP, our filings and financials dataset can be used inside ChatGPT and Claude, while still returning citations for every figure.2 That is useful when we want to stay in the same conversation instead of jumping between a terminal, a browser, and a spreadsheet.

The workflow is straightforward. We ask the assistant to search filings, pull the relevant financials, compare companies, and summarize the answer with source pages attached. Because every read tool returns page IDs, we can audit the result against the report page. That makes the answer usable for internal notes, not just for quick discovery.2

This is also where the supporting keywords matter in practice. Teams comparing options often search for LSEG equity research reports or AlphaSense equity research because they want breadth and search. Those tools can help with document discovery and broader research. Our workflow is narrower and more specific: filings first, comparable financials second, and source-linked answers throughout. LSEG Workspace also highlights research productivity, transcript search, and auditability in its workflow materials, which shows how central source-linked research has become across the market.45

For teams building a repeatable process, the question is not only how to get equity research reports. It is how to turn a report into a verified comparison and then into a shareable output without losing the source trail.

Turn findings into tables, reports, and shareable workspaces

Research becomes more useful when it can be handed off. That is why we built workspaces and report tools around the research process itself. We can collect findings, notes, and analyses in one workspace, then turn them into a report with tables and charts. We can also export to PDF, Word, and Excel.1

For team workflows, the important part is not the export button alone. It is that the underlying evidence stays tied to the result. If a colleague opens a table in a workspace, they should be able to trace a figure back to the filing page that produced it. That is how a screening result becomes an internal memo, and how a memo becomes a model input or investment discussion.

We also use VisualCards for figures and findings that need to travel quickly across a team. The source is marked on every card, which makes it easier to share a chart or stat without breaking the evidence chain.1

Concrete example: if we want to compare two Nordic industrial companies against a US peer group, we can search the latest interim reports, confirm revenue and margin lines on the source page, compare the companies in one table, and then place the results in a workspace report for review. The same path works for a single-company update, a peer set, or a sector screen.

Frequently asked questions

What makes an equity research data platform useful for analysts?

It should connect filings, financial statements, comparisons, and source-linked answers in one workflow. The key is not just access to data, but the ability to verify every figure against the underlying report.1

How do we research public companies faster?

We start with the filing, use the answer to locate the page, verify the number, and then compare it against peers in the same data model. That cuts down on manual reconciliation and makes the output easier to defend.

Can we use the workflow for both US and Nordic companies?

Yes. Our data model covers US, Finnish, and Swedish public companies, and it supports comparisons across those markets in one table.1

How does MCP help with equity research?

MCP lets ChatGPT, Claude, Cursor, and other clients call the same filings and financial tools we use elsewhere, while keeping citations attached to the result.2

Where should we look if we want company-level detail?

Start with the company pages. They combine business profile, financial statements, filings, and links to original reports in one place.1