Solution

Company Comparable Analysis Automation

For equity research, corporate development, and investment teams: peer metrics pulled from filings and transcripts, normalized to one taxonomy, and cited to the exact source.

10-K10-QEarnings transcriptsInvestor presentationsPress releases
100% of numbers cited to sourceOne taxonomy across every reporting styleFlagged outliers, human-approved

The problem

Why this exists

Hours

Extraction eats the analysis

Analysts hunt through 10-Ks, transcripts, and decks for KPIs before any thinking starts. The scavenging takes the day; the judgment gets the leftovers.

3 names

Every issuer reports differently

One company reports Subscription Revenue, another Recurring Services, a third ARR. Hand-mapping labels across a cohort is slow, subjective, and quietly inconsistent.

2 metrics

Depth rationed by bandwidth

Manual comps stop at revenue and EBITDA because time runs out. CAC, net retention, and Rule of 40 never make the sheet.

The product, not a promise

A comp set you can interrogate

Company Comparable Analysis Automation — workspace
Cohort loaded — filings, transcripts, decks per issueringestedcited
As-reported labels mapped to the standard taxonomyone basiscited
Fiscal years aligned, currencies convertedcited
Non-GAAP definition changed versus prior period — reviewverify
Every metric linked to its source sentence or cell1 clickcited
HUMAN-APPROVED BEFORE IT POSTS

How it works

File in. Answer out.

  1. 1

    Ingest

    Filings, transcripts, investor decks, and press releases load for every company in the cohort.

  2. 2

    Extract

    Reported metrics are pulled with their context — as-reported labels, periods, currencies, adjustments.

  3. 3

    Normalize

    Disparate labels map to one standard taxonomy; fiscal years, currencies, and non-GAAP adjustments are aligned.

  4. 4

    Compare

    The cohort renders on the same basis — margins, retention, efficiency — with outliers flagged.

  5. 5

    Audit

    Any number clicks through to the exact sentence or table cell in the source document.

Who it's for

Built for the people who own the outcome

Research analyst

The day starts at analysis, not extraction.

  • KPIs arrive from filings, transcripts, and decks already normalized
  • Granular metrics — CAC, NRR, Rule of 40 — cost the same effort as the top line
  • Any figure verifies in one click to the source sentence or cell

Head of research

Coverage scales without headcount.

  • A 50-name cohort is as tractable as five direct competitors
  • One taxonomy means every analyst's comps agree with each other
  • Comps refresh with each new filing instead of each quarterly rebuild

Compliance & IT

Cited inputs, governed process.

  • Every metric traces to its exact location in the source document
  • Outliers and low-confidence extractions flag for human review
  • Extractions, mappings, and approvals are logged end to end
Equity researchCorporate developmentPrivate equityInvestment bankingCredit researchStrategy teams
100%of metrics cited to the filing
One taxonomyacross every reporting style
Full cohortlarge peer sets as easily as small
Zerotemplate maintenance per issuer

Most of the effort in comparable analysis is extraction, not analysis. Analysts spend the bulk of their time hunting through 10-Ks, transcripts, and press releases for KPIs, then arguing spreadsheets into agreement — because every company reports differently, and mapping labels by hand is slow and subjective. This solution does the scavenging and the normalization, and leaves the judgment to the analyst.

From filings to a common basis

The platform ingests public filings, earnings transcripts, investor presentations, and press releases across the coverage set. Because it reads financial context rather than matching keywords, it maps each company’s reporting labels to a single standard taxonomy, aligns fiscal periods, converts currencies, and keeps as-reported and adjusted figures distinct. One issuer’s “Subscription Revenue” and another’s “Recurring Services” land on the same line by policy, rather than by whoever built the spreadsheet.

Depth stops being rationed. Manual bandwidth usually limits comparison to revenue and EBITDA; here, granular operating metrics — CAC, net retention, R&D efficiency, Rule of 40 — are extracted with the same effort as the top line. When a peer’s metric moves, the surrounding context — an acquisition, a restatement, a definition change — is captured alongside it, so the “why” travels with the number.

Numbers a committee can trust

Every extracted metric links to its exact source — the sentence in the transcript or the cell in the filing table — so any figure in the comp set can be verified in one click. Outliers and low-confidence extractions are flagged for human review rather than silently included; the analyst approves what the model surfaced before it ships. That is what makes the output usable in valuation work and investment memos, where a wrong comp is worse than a missing one.

Coverage scales the way headcount cannot: a cohort of 50 industry players is as tractable as the five direct competitors you track today, and the comp set stays current with each new filing instead of aging in a quarterly report.

Objections, answered

What teams ask us first

How do I trust an extracted metric?

Every number links to the exact sentence or table cell it came from, so verification is one click rather than a re-derivation. Outliers and low-confidence extractions are flagged for analyst review instead of silently included.

Can it use our house taxonomy and definitions?

Yes. Labels map to your standard taxonomy, as-reported and adjusted figures stay distinct, and a definition change at an issuer is surfaced rather than papered over.

What does the audit trail look like?

Each figure in the comp set carries its source citation, its taxonomy mapping, and the reviewer who approved flagged items. A committee can trace any number in a memo back to the filing months later.

How long does deployment take?

There are no per-issuer templates to build or maintain. Setup is loading the cohort and confirming the taxonomy mapping; coverage expands by adding names, not by engineering.

Bring your hardest comp set.

Watch a full cohort extract, normalize to one taxonomy, and render on a common basis — every number cited — live in the demo.

Request a demo