Solution
For credit analysts, deal teams, and strategy groups: a normalized peer comparison where every figure traces back to the filing it came from.
The problem
Most of a benchmarking exercise is copying numbers out of PDFs into a spreadsheet, one peer at a time.
Formats, definitions, and disclosure depth all vary — so 'comparable' metrics quietly aren't.
A new peer, a new period, a new reporting format — and the comparison rebuilds from zero instead of updating.
The product, not a promise
How it works
Pull annual reports, filings, earnings releases, and investor materials for the full peer set.
Extract metrics and disclosures into one consistent structure, whatever each company's reporting format.
Line up performance, trends, and narrative differences across the peer group.
Deliver a benchmarking pack where every number traces back to the page it came from.
Who it's for
Credit / research analyst
Head of credit / deal lead
Model risk & audit
Peer comparison sounds simple until you do it at scale. Every company reports differently — different formats, different definitions, different levels of disclosure — and the analyst assembling the comparison spends most of the time re-keying numbers from PDFs instead of interpreting them. When the peer set grows or the quarter turns, the work starts over.
Botminds collects the raw material for the full peer set — annual reports, regulatory filings, earnings releases, investor presentations, rating reports — in whatever format it arrives, scanned or digital. Metrics, disclosures, and management commentary are extracted into one normalized structure, so revenue is revenue and coverage is coverage across every company in the set.
From that base, the comparison is mechanical rather than heroic: line up performance across the group, track how each company’s metrics and narrative shift period over period, and flag where a borrower or target deviates from its peers. Credit and risk teams use the same peer view to position an obligor within its industry; deal teams use it to test a target’s story against companies that have already published theirs. The method is identical every time it runs, which makes quarter-over-quarter comparisons honest.
A benchmark that ends up in a credit memo or an investment committee paper has to survive the question “where did that number come from.” Here the answer is built in: every extracted figure links to the exact source page, every normalization step is logged, and conflicting disclosures are flagged for the analyst instead of silently averaged. Human review gates the final pack. The output is decision-ready intelligence with its evidence attached — regulator-ready by construction.
Objections, answered
Every extracted figure links to the exact source page, so checking it takes one click. Conflicting disclosures are flagged for the analyst rather than silently averaged, and the pack ships only after human sign-off.
Yes. Your definitions drive the normalization — how you compute leverage, coverage, or margin — and your peer sets are saved and rerun each period, so the comparison stays consistent with your house methodology.
The evidence is attached by construction: citations on every figure, a log of every normalization step, and a recorded approval. The platform is ISO 27001 and SOC 2 certified, deployable in your cloud or on-prem.
Weeks. Setup is defining your metrics and loading the peer set's documents; the first pack is produced during onboarding, on your own companies.
Watch a stack of annual reports become one normalized comparison — every figure cited — during the session.
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