Comparable Company Research from AI Filings Easily (Step by Step)

Quick Answer

Comparable company research becomes faster and more defensible when AI extracts the same defined fields from each issuer's filings, preserves the source location, and flags differences for analyst review. The goal is not a generic summary. It is a traceable comps record that can support an investment committee memo, compliance review, or diligence decision.

Introduction

Manual comparable company research can consume hours per company because analysts must reconcile annual reports, quarterly updates, and disclosures that use different labels and reporting periods. AI-driven corporate research reduces that collection burden when the workflow starts with a disciplined peer definition and ends with a citation for every material datapoint. Domestic issuers submit annual Form 10-K reports, quarterly Form 10-Q reports, and Form 8-K current reports for specified events. A missed footnote or unmatched fiscal period can change the conclusion more than a polished valuation multiple.

Key Takeaways:

• Define peer criteria before asking an AI agent to collect data.

• Require a filing citation for every financial figure and risk signal.

• Normalize dates and accounting treatment before comparing valuation outputs.

How Do You Build a Repeatable Comparable Company Research Process?

A repeatable process starts with written screening criteria, not a peer list that looks convenient after the multiples are already known. Comparable company research is reliable only when each company enters the dataset for the same business reason: business model, customer type, revenue profile, geography, and adjacent product category, with exclusions documented before anyone reviews the financials.

Step 1: Define the peer set and research fields

Write the screening criteria as instructions an analyst and an agent can both follow. For an enterprise software target, adjacent product offerings are more relevant than a broad technology label, and each strong peer candidate should go through a company research brief to confirm fit before data collection begins.

• Business model: Match recurring, transactional, or mixed revenue structures.

• Customer profile: Separate enterprise, mid-market, and consumer exposure.

• Geography: Note markets that materially affect regulation or demand.

• Reporting period: Capture fiscal year-end and latest reported quarter.

• Disclosure fields: Set required metrics, risks, and source references.

Step 2: Create a filing-first source map

Use the filings themselves as the primary record, not investor presentation language or an AI-generated web summary. The SEC's official Form 10-Q guidance is a useful reference when reviewing quarterly filings. Read the official 10-K filing guide for business narrative, risk factors, management discussion, financial statements, and notes; use the latest quarterly filing to identify changes since the annual report. Domestic issuers submit annual Form 10-K reports, quarterly Form 10-Q reports, and current Form 8-K reports for specified events. A Form 10-Q is filed for each of the first three fiscal quarters, which makes it essential for preventing a stale annual figure from driving a current comparison. For a wider look at platforms built specifically around this workflow, see SEC filing software.

How Does AI Extract Comparable Evidence from Company Filings?

AI extracts comparable evidence by applying one fixed extraction schema to every issuer’s filings and returning the value, period, unit, and a supporting excerpt for each field. Once the scope is fixed, custom AI agents for financial services can collect those recurring fields across issuers without forcing every analyst to search the same terms manually.

Step 3: Run a structured extraction request

Prompt the agent with a fixed schema rather than an open-ended request for insights. Request revenue, profitability measures, cash flow, debt, share count, segment disclosures, material risk changes, and management commentary only where the filing states them. Add a separate field for non-recurring items because one-time acquisition integration costs and repair costs following events such as a hurricane can distort comparisons.

For each output, require the company name, filing type, filing period, page or section, extracted value, units, and an exact supporting excerpt. This turns a response into an auditable record for enterprise due diligence instead of a set of assertions that must be researched again during review. Where filings use different terminology, preserve the original label alongside the normalized field.

Step 4: Separate disclosed facts from analyst judgment

AI for investment diligence teams should classify evidence before it interprets it. A disclosed revenue figure is a source-backed fact; a statement that a risk factor matters more than a peer's is an analytical judgment that needs an explicit rationale. Keep those categories in different columns so a reviewer can validate facts without debating the investment thesis.

Use filings disclosure monitoring when the research continues beyond a single snapshot. New quarterly reports, leadership changes, and regulatory disclosures can alter the underlying record after the initial comps table is prepared.

How Does Grep Compare to AlphaSense for Comparable Company Analysis?

AlphaSense is built primarily as a search engine across filings, earnings call transcripts, and broker research, so it is strongest at helping an analyst find a relevant passage quickly. Grep’s agents are built to assemble the structured output itself: a dated, cited comps table with the peer definition, metrics, and sourcing already in place, rather than a set of search results an analyst still has to compile by hand.

Neither approach replaces analyst judgment on which peers belong in the set or which risk factors matter most. For a wider look at how platforms in this category differ on search coverage, citation depth, and reporting formats, see this company analysis tools comparison.

How Do You Turn Filing Data Into a Defensible Comps Table?

A defensible comps table normalizes inputs before calculating multiples and keeps the calculation logic visible, so a reviewer can retrace every number back to its filing. Calendarize financials when fiscal year-end dates differ, identify the period behind every metric, and remember that trading comps set valuation parameters rather than one precise answer, so the range and assumptions matter as much as the selected multiple.

Step 5: Normalize inputs before calculating multiples

Normalize only fields with a documented basis. Align reporting periods, standardize units and currency labels where applicable, and isolate non-recurring items rather than silently adjusting them away. When companies have different fiscal year-end dates, calendarize their financials to a common timeframe before comparing them. The following framework shows what a reviewer should be able to inspect before accepting an output.

Do not ask an AI agent to conceal missing data by estimating it. A blank, clearly labeled as not disclosed in the filing, is more useful than a fabricated value in a board-facing spreadsheet.

Build the final table from peer metrics comparisons that retain their underlying citations. This creates a clean handoff between the analyst compiling data and the senior reviewer testing assumptions.

Step 6: Test the valuation output and disclosure trail

Validate the comp set through two checks: whether the selected companies remain operationally comparable and whether every derived value can be reconstructed from cited inputs. Applying a peer group’s EV/EBITDA multiple to the same target can produce a different implied value depending on whether the peer median or the peer mean is used, which is why the aggregation choice should stay visible rather than collapse into one answer.

A reviewer should be able to move from a conclusion to the calculated multiple, then to the source values, and finally to the original disclosure. That chain is what makes reports defensible for board review, especially when the peer set or accounting adjustments are challenged.

How Do You Keep Comps Research Reviewable After the First Memo?

Comps research stays reviewable when the peer criteria, extraction schema, calculations, reviewer notes, and citations are preserved as a reusable operating record instead of rebuilt from scratch each time. That record compounds in value with every refresh and makes it easier to explain why a company entered or left the set between diligence cycles.

Step 7: Establish review gates and continuous monitoring

Set approval gates for peer selection, source extraction, normalization, and final conclusions. A second reviewer should verify material figures directly against the filings, while the lead analyst resolves classification disputes before the table reaches an investment committee. Because Form 10-Q reports are filed for each of the first three fiscal quarters, each new quarterly filing is a practical trigger to review financial and risk disclosures.

Step 8: Move from one-time research to monitored diligence

For recurring reviews, configure alerts around the specific signals that could change a peer comparison: new filings, revised guidance, leadership changes, material litigation disclosures, financing activity, or shifts in disclosed risk. Comparable deal analysis can sit alongside trading comps when the diligence question also requires transaction context.

Source citations should be part of the process from the first extraction, not added after conclusions have already circulated. At Grep, Agent can produce traceable, citation-backed reports, spreadsheets, and slide decks for high-stakes research, while Loops and Monitors support scheduled or event-triggered refreshes and always-on screening.

Conclusion

Start with a defined peer set, collect data from primary filings, normalize only what the record supports, and retain a citation trail for every material conclusion. This approach lets investment and compliance teams reduce repetitive search work without lowering the review standard. For teams that need custom agents built around high-stakes diligence workflows, Grep provides traceable research outputs and ongoing monitoring rather than a one-time summary. The result is a comps package that can move from analyst desk to reviewer with its evidence intact.

Ready to make filing research reviewable from the start? Get started with Grep and build a source-backed research workflow.

Frequently Asked Questions (FAQs)

How do custom AI agents support corporate due diligence?

Custom AI agents support corporate due diligence by applying a fixed extraction schema across filings, preserving source references, and separating disclosed facts from analyst interpretation so reviewers can test the work without recreating every search.

Can AI agents generate defensible reports for boards?

AI agents can generate defensible reports for boards when every material claim includes a traceable source, reporting period, calculation logic, and enough quoted context for a reviewer to distinguish documented evidence from the analyst's conclusion.

Is AI research accurate enough for financial compliance?

AI research is accurate enough for financial compliance when it operates within defined fields and mandatory human review, because compliance teams must validate source context, periods, exceptions, and final judgments rather than accepting output as self-verifying.

What makes an AI research platform audit-ready?

An AI research platform is audit-ready when it preserves exportable decision trails, source citations, the sequence of inputs and conclusions, and controlled access practices that allow a reviewer to reconstruct the decision after the work is complete.

How does persistent memory improve AI research output?

Persistent memory improves AI research output by retaining approved peer definitions, recurring terminology, reviewer decisions, and prior research context, which reduces inconsistent classification when the same diligence workflow is repeated over time.

How to find comparable company data from SEC filings using AI?

To find comparable company data from SEC filings using AI, define the peer set and fields first, direct the agent to primary filings, require filing-level citations for each extracted value, and normalize reporting periods before calculating comparisons.

About the Author

David Aviles is Head of GTM at Grep, with experience building go-to-market functions at seed-to-scale companies including Optimizely, Amplitude, and Mintlify. His work focuses on how B2B teams adopt reliable systems for high-stakes work without creating more operational overhead.