benchmark

name

benchmark

description

Operational peer / comp discovery for US public companies via deepKPI semantic search — not valuation multiples. Build KPI fingerprint from SEC data; company_summary_search (same-vertical first); align KPIs; diff gaps → segment sub-benchmarks. Outputs: tight chat 1-pager, optional Revelata HTML, optional Excel (.xlsx). Use when comparing to peers, finding comps or most-similar companies, benchmarking, comp sets, valuation peers, thematic "what's like X?", or segment pure-plays. Exclude unrelated verticals (e.g., gym vs car wash) except clear segment-to-segment, same customer + same need.

benchmark

Find comparable public companies ("benchmarks") for a target company or segment using deepKPI. The output is not a mechanical list of industry peers — it's a curated set where every benchmark adds a specific analytical lens, and where the gaps in coverage are as informative as the matches.

Do not pair companies that do wildly different work — different end customers, different underlying need, different operating model — just because semantic search surfaced them together (e.g. a gym chain and a car-wash operator). That is not a useful whole-company benchmark. The only exception is when a specific segment of each business plausibly targets the same customers with products that serve the same job-to-be-done; in that case, treat the match as segment-to-segment (Step 3 sub-benchmarks, clearly labeled), not as "these two conglomerates are comps."

Vertical first, then wider. Build the core benchmark set from companies in the same vertical as the target's primary business (same industry, same customer job, same operating shape) before you add names from outside that vertical. Broader or cross-vertical searches are for filling gaps after same-vertical peers are in place, for a user-requested "outside the sector" lens, or for Step 3 segment sub-benchmarks — not as the default first pass.

The workflow is explicitly iterative. Segment sub-benchmarks are not pre-assumed based on what segments exist; they are discovered by diffing the target's KPI fingerprint against what the whole-company benchmarks can actually explain. If something in the target's operations has no counterpart in the whole-company set, that's the signal to go deeper.

Data source

See ../_common/data-source-rules.md for the runtime routing table and the canonical inventory of deepKPI operations.

Hard stop if deepKPI access fails

See ../_common/connection-failure.md. STOP and ask before falling back.

Dependencies

Before starting, consult:

• The kpi skill — retrieval workflow, provenance rules, scaling artifact handling, KPI search strategy. Required for all data pulls.
• ../_common/revelata-excel-styling-rules.md — only if user requests Excel output.
• The implied-metric skill — when filling Q4 or segment gaps in candidate alignment.

Every number displayed must have a [value](provenance-url) link. No exceptions.

Output mode — ask first

Before pulling any data, ask the user which output format they want:

"Would you like an interactive HTML report (opens in browser, clickable provenance links, Chart.js time-series charts) or in-chat markdown output? Or both?"

• If HTML: generate the full HTML report (dark Revelata branding, Chart.js line charts for KPI trends over time, fingerprint grid). Also produce the chat 1-pager as a compact summary.
• If in-chat: produce only the chat 1-pager with full markdown provenance links (see rules below). Offer the HTML at the end.
• If both: produce chat 1-pager first, then generate the HTML.

Offer Excel at the end regardless of which mode is chosen, unless the user already requested it.

Outputs

Format	When	Description
Chat 1-pager	Always	Fingerprint pill rows + master table + KPI alignment + ≤5 notes — all values hyperlinked
HTML report	If user chose HTML or both	Dark Revelata-branded report with fingerprint grid, benchmark cards, and Chart.js KPI trends over time (line charts: target vs benchmarks on a shared period axis)
Excel workbook	User requests .xlsx	KPI alignment table, periods as columns per ../_common/revelata-excel-styling-rules.md

In-chat provenance link rules (non-negotiable)

Every numeric value shown in chat must be a markdown hyperlink to its source filing passage. No bare numbers.

Format: [+4.9%](https://www.revelata.com/docviewer?pvid=11240) — the pvid comes from the deepKPI response.

This applies everywhere in the chat output: fingerprint bullets, the KPI alignment table, notes, and the diff insight. If a value has no pvid (e.g. an estimate or derived metric), label it explicitly as ~$X est. or implied with no link. Never omit the link for a directly sourced value.

Example of correct in-chat KPI alignment table row:

| TXRH | [+4.9%](https://www.revelata.com/docviewer?pvid=11240) | [$8.7M](https://www.revelata.com/docviewer?pvid=10061) | [714](https://www.revelata.com/docviewer?pvid=11537) |

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Workflow

The four steps below run in sequence. Steps 1–3 are analytical; Step 4 is output generation. Run all deepKPI calls in parallel wherever possible to save time.

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Step 1: Build the target's KPI-enriched fingerprint

This is the foundation everything else builds on. Pull it from deepKPI — do not use general knowledge for specific metrics.

Calls: query_company_id → get_company_summary → get_company_segments → list_kpis → search_kpis (for each segment's signature metrics)

What to extract:

A. Narrative — 2-3 sentences: business model, revenue drivers, customer type, go-to-market, geography. Ground it in numbers where possible (unit count, revenue scale, market share if stated).

B. Segment map — For each reportable segment:

• Name and revenue share %
• One-sentence description of the economic model (how it makes money, who pays)
• 3-5 signature KPIs with actual values from deepKPI, each with provenance link

The segment KPIs are what make this fingerprint useful for diffing. For an airline: ASMs, load factor, CASM, TRASM. For a refinery: capacity bpd, utilization %, gross margin per barrel. For a restaurant segment: unit count, comp sales %, AUV. Pull the real numbers.

C. KPI signature — The 3-5 metrics that matter most for the consolidated company. These become the columns in the KPI alignment table in the output.

Output of Step 1: An internal working document (not shown to user) with:

Target: [TICKER] — [Company] (CIK: NNNNNN, FY: DATE)
Primary vertical (for comp search ordering): [e.g. full-service restaurant, regional bank, SaaS HCM]
Narrative: ...
Segments:
  [Segment A] (XX% rev): [description] | KPIs: metric1=[val](url), metric2=[val](url), ...
  [Segment B] (XX% rev): [description] | KPIs: metric1=[val](url), ...
KPI signature: metric1, metric2, metric3 [, metric4, metric5]

---

Step 2: Find candidates and align their fingerprints

A. Search

Run company_summary_search with 3-5 queries. Each query should describe a business model, not just a sector name. The goal is semantic richness — describe what the company does, for whom, how it earns money, and what drives its unit economics.

Order matters — same vertical first. At least the first two queries must stay tightly inside the target's primary vertical (from Step 1: summary, segments, and the "Primary vertical" line). Pull and KPI-align those results before you run looser or cross-vertical queries. Use additional queries to go outside the vertical only after you have a credible same-vertical candidate set, or when the user explicitly wants analogies from other sectors, or when Step 3 needs a segment-specific search.

Examples of the contrast:

• Weak: "airline" → Strong: "hub-and-spoke network carrier international domestic routes premium cabin loyalty co-brand revenue per ASM"
• Weak: "restaurant chain" → Strong: "multi-brand casual full-service dining operator franchised same-store sales average unit volume"
• Weak: "software company" → Strong: "B2B SaaS subscription recurring revenue net revenue retention enterprise mid-market"

Use top_k_companies: 10-12 per query. Deduplicate across queries. Target 6-12 candidates.

B. KPI alignment

For each candidate: call list_kpis (free), then search_kpis for the target's KPI signature metrics (from Step 1C). This produces aligned rows — the target's metrics sitting next to each candidate's equivalent metrics.

Recency rule (do this from the start): when the user's question is about "current" / "latest" comparability, prefer the most recent quarterly periods available (10-Q-derived) for each KPI over stopping at the last 10-K annual figure. Use annual series for long-cycle context, but do not ship a benchmark set that effectively says "data is old" if newer 10-Q periods exist for the KPI.

The alignment reveals two things:

1. How close each candidate is on the dimensions that matter (similar range = structurally comparable)
2. Where candidates have no data or wildly different values (poor fit on that dimension)

Coverage coding:

• Green: metric present and plausible
• Yellow: metric present but value seems off (scaling artifact, possible cross-contamination)
• Red: no data

C. Tier the candidates

Based on the aligned KPI fingerprints (not just the description similarity), and only after passing the structural fit gate below:

• Tier 1: Closest structural match — same business model and same customer/need, similar values on 3+ signature KPIs
• Tier 2: Useful comparators — same overall job-to-be-done; good match on some KPIs, different on others; adds a specific lens
• Tier 3: Same kind of business as the target, different scale, mix, or geography — still a coherent comp (e.g. regional vs national airline). Not unrelated verticals "for color."

D. Structural fit gate (drop before the output table)

Before anything appears in the chat 1-pager or HTML as a whole-company benchmark, ask: Would an investor reasonably describe this company as doing the same job for the same type of buyer as the target? If no, exclude it from Tier 1–3 for whole-company analysis — even if a few metrics line up by coincidence. If there is a real but narrow overlap (same wallet, same purchase occasion, same enterprise buyer for a specific product line), keep the candidate only as a segment sub-benchmark in Step 3 with an explicit "Maps to: Segment …" story, not as a peer for the consolidated company.

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Step 3: Diff — find what the whole-company benchmarks don't explain

This is the step that surfaces the most analytically valuable insights.

The question: After aligning KPI fingerprints in Step 2, which parts of the target's operations have no good coverage in the whole-company benchmark set?

A "coverage gap" exists when:

1. The target has a reportable segment whose signature KPIs appear nowhere in the Tier 1/2 benchmark set, OR
2. The target reports KPIs that are structurally foreign to all benchmark candidates (e.g., refinery throughput bpd when all candidates are airlines), OR
3. A major segment has a fundamentally different economic model than what any whole-company benchmark represents

For each gap identified:

a. Build a segment-specific fingerprint using the segment's own KPIs from Step 1B ("this segment, if it were a standalone public company, would look like:")

b. Run a fresh company_summary_search describing that segment as a standalone business. Sub-benchmarks must still satisfy same customer + same need for that segment; do not pull unrelated verticals "for coverage." Use the segment's KPIs as the anchor — e.g., for a refinery segment: "independent petroleum refiner crude oil throughput barrels per day capacity utilization refining margin per barrel crack spread". Not: "refinery".

c. Pull KPI alignment for sub-benchmark candidates using the segment's KPI signature (not the parent company's consolidated metrics)

d. Tag all matches as Maps to: Segment: [name] in the master table

Important framing: The sub-benchmark tells you what that segment would look like as a standalone profit-maximizing business. When the segment operates under a different economic logic inside its parent (cost-center, captive supplier, hedge), say so explicitly — the gap between the sub-benchmark's economics and the segment's implied economics is the analytical insight.

Example of the diff in action:

• Target: Delta (airline + refinery)
• Step 2 finds: UAL, AAL, LUV, ALK — all cover the airline KPIs (load factor, CASM, ASMs)
• Diff: refinery throughput bpd, utilization %, gross margin/bbl appear in Delta's segment map but in none of the airline benchmarks
• Step 3 triggers: build refinery segment fingerprint → search → find PBF, CVI, DINO as sub-benchmarks → align on throughput/utilization/margin
• Insight that emerges: Monroe's 200K bpd capacity is closest to CVI (207K bpd combined); PBF shares East Coast PADD 1 geography; but Monroe runs as a hedge, not a profit center — the merchant refiner margins ($8–16/bbl) represent the opportunity cost Delta pays to own it

The segment sub-benchmark logic is not limited to named segments. If the target has any operational dimension that reads as structurally foreign to the whole-company comps — a financial arm, a logistics network, a retail footprint embedded in a manufacturer — ask whether a standalone pure-play exists in the S&P 1500 that illuminates it.

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Step 4: Build outputs

Chat 1-pager

Tight. No prose sidebars. Everything fits on one screen. In the benchmark table and KPI alignment table, put same-vertical names first (clearest structural peers in the primary vertical), then cross-vertical or segment-mapped comps, with Maps to spelling out any segment story.

# Benchmarks for [Company] ([TICKER])

[TARGET FINGERPRINT — 2-3 sentences. Every sourced metric hyperlinked:
e.g. [+2.0% blended comps](https://www.revelata.com/docviewer?pvid=578),
[$12.1B revenue](https://www.revelata.com/docviewer?pvid=11442)]
Segments: [Segment A] XX% rev | [Segment B] XX% rev

## Benchmark table

| Ticker | Maps to | Similar on | Different on | Scale vs [TARGET] |
|--------|---------|------------|--------------|-------------------|

## KPI alignment — latest available period(s)

| Company | [metric1] | [metric2] | [metric3] | [segment metric] | Period |
|---------|-----------|-----------|-----------|-----------------|--------|
| [TARGET] | [val](pvid-url) | [val](pvid-url) | [val](pvid-url) | [val](pvid-url) | FY... or FY..-Q.. |
| BENCH1   | [val](pvid-url) | [val](pvid-url) | — | — | FY... or FY..-Q.. |

Column headers are plain text. Only the value cells carry hyperlinks.
Estimates/derived values: `~$X est.` with no link.

## Notes
- [≤5 bullets — only material caveats: scaling flags, cross-contamination, structural differences,
  coverage gaps, economic logic mismatches (cost-center vs. profit-maximizer)]

## Sources
[Ticker (CIK) — Green/Yellow/Red — FY end — any corrections]

If a section would be empty, omit it. If the KPI alignment table would exceed 10 rows, drop Tier 3 from it (keep Tier 3 in the benchmark table only).

HTML report

Read references/html-template.md. Key structure:

• Hero fingerprint grid (table.fp-grid): KPIs × benchmark columns only — no prose inside the grid (no "Why it matters", thesis blurbs, or fp-context-style rows). Put narrative in benchmark cards and diff insight. .legend-strip (color key) sits below the grid, not in the page header.
• Benchmark cards (one per company): order same-vertical peers before cross-vertical or segment-mapped names. Tier-colored top border, similar/different columns, key KPI values hyperlinked. Segment sub-benchmarks use purple border + "Segment:" badge.
• KPI trends over time (charts + alignment table): This section is where deepKPI's edge shows up—filing-resolved time series most vendors do not reconstruct at this depth. For the 2–3 most important signature KPIs, pull multi-period values for the target and every benchmark (prefer quarterly where coverage is solid; otherwise annual—follow the kpi skill for period selection, scaling, and provenance). Render Chart.js line charts (not single-period grouped bars): x-axis = time (ordered FY/FQ labels), one line per company so the reader compares trajectory, inflection, and pace across competitors on the same metric. Put the full KPI alignment table (with hyperlinked values) below the charts as today. If a KPI truly has fewer than three comparable periods across the set, you may show a sparse line chart or a one-line caption pointing readers to the table—do not imply a cross-sectional bar is equivalent to the time-series view.
• Chronology QA for every time-series chart (mandatory): Ensure the plotted points are in chronological order for every company series and the x-axis labels are in ascending time. Do not rely on input order. Sort by a real time key (prefer period end date; otherwise parse FY/FQ labels into a comparable key) before passing points to Chart.js. A zig-zagging line is almost always an ordering bug.
• Y-axis QA for every time-series chart (mandatory): Double-check each chart's Y scale before shipping HTML. The visible min/max domain must include every non-null plotted point across all series (target + benchmarks). Do not set fixed min/max, suggestedMin/suggestedMax, or beginAtZero unless those bounds are recomputed from the union of all series values—wrong bounds clip lines off-canvas so they look missing. Prefer automatic scaling plus padding (e.g. Chart.js scales.y.grace as a %) so tight clusters stay visible. For log scales, omit non-positive points or avoid log if that would drop the series; confirm lines still render. Mentally verify each tab: any ticker with data must show a visible polyline or points—not a legend entry with no trace.
• Diff insight box (cyan glow, same as synthesis in pressure test): 2 paragraphs on what the benchmark set reveals that whole-company analysis would miss.
• Notes and Sources footer.

Excel workbook

Follow ../_common/revelata-excel-styling-rules.md. One sheet for whole-company benchmarks, one sheet per segment sub-benchmark group. Annual periods as columns; green cells for target values; clickable provenance links.

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Failure modes

• Assuming segment sub-benchmarks without doing the diff: Don't pre-decide "this company has a real estate arm so I'll find REITs." Let the KPI gap analysis surface it. The diff step earns its keep precisely because the gaps are often surprising.
• Narrative-only fingerprint: If Step 1 doesn't pull actual KPI values, the diff in Step 3 has nothing to work with. The fingerprint must have numbers.
• Prose inside the HTML hero grid: Do not add "Why it matters", thesis blurbs, or other commentary rows inside table.fp-grid — the grid is KPI labels and numbers only. Use benchmark cards and diff insight for narrative (references/html-template.md).
• Skipping KPI alignment for candidates: Checking description similarity is not enough. Pull the actual metrics — two companies can describe themselves similarly but operate very differently when you look at unit economics.
• Treating all segments equally: Not every segment warrants a sub-benchmark search. Focus on segments where: (a) the parent discloses separate KPIs, and (b) the KPIs are foreign to the whole-company benchmark set.
• Time series plotted out of order: If any chart line zig-zags "backward," the points are not time-sorted. Sort each series by period end date (or FY/FQ key) before charting, and make sure the shared x-axis label list is in ascending order too.
• Y-axis clips time series in HTML: Hard-coded or guessed Y bounds, or beginAtZero used without checking the data band, often hides entire lines outside the drawable range. Always derive bounds from all series (or use defaults + grace) and re-check before delivery.
• Too much prose in chat: The 1-pager is a reference artifact. Cut until it hurts.
• Skipping list_kpis: Free call that prevents wasted credits on metrics that don't exist.
• Unrelated verticals as comps: A gym and a car wash are not whole-company benchmarks for each other unless you have a defensible segment-level story (same customers, same need). If you cannot write that one sentence without hand-waving, drop the name.
• Cross-vertical before in-vertical: Do not foreground or prioritize outside-the-vertical names until same-vertical peers are identified and KPI-aligned. If the first search pass drifts cross-vertical, reset with tighter in-vertical queries before presenting the table.