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Illustrative report preview

What a Kovmere report looks like

Fictional example data shown to demonstrate the report format. Your report is generated from your own measured answers.

Six sections, one decision at a time

Summary · Competitive position · Buyer & engine gaps · Content & citation gaps · 30-day plan · Evidence

Where you stand

Example Brand is visible—but rarely the first recommendation.

Won: 6 of 18 (33%)Partial: 4 of 18 (22%)Lost: 8 of 18 (44%)8lostWon 6Partial 4Lost 8

41% recommendation rate (110 of 270 judged answers recommended Example Brand) · ranked 2 of 4 brands measured.

Who wins instead

Northwind is recommended 1.8× as often as Example Brand — 31 percentage points ahead on the same 270 judged answers.

0%25%50%75%100%NorthwindNorthwind: recommended in 194 of 270 judged answers (72%)72% · 194/270Example BrandExample Brand: recommended in 110 of 270 judged answers (41%)41% · 110/270ContosoContoso: recommended in 64 of 270 judged answers (24%)24% · 64/270FabrikamFabrikam: recommended in 40 of 270 judged answers (15%)15% · 40/270

Recommended in 270 judged answers · 5 of 5 engines measured.

Buyer questions, grouped ↓

What to fix first

creator-guides.example is cited on 5 buyer questions you did not win and does not name Example Brand

5 buyer questions covered · https://creator-guides.example/how-to-get-paid-faster · 2 actions in the plan.

The action plan ↓

Buyer questions, grouped the way buyers decide

Every question shows its category, outcome and how each engine answered.

Scroll for more →

Buyer-decision categoryQuestionsWonPartialLost
Fit for my situation4112
Comparison and switching3012
Problem-aware: what solves my problem4211
Category discovery: which providers exist3111
Constraints: pricing, terms and limits3201
Demand-driven: how buyers phrase it1001

8 of 18 buyer questions never recommended Example Brand; 4 did on some repeats; 6 on every repeat. OpenAI recommends Example Brand in 48% of its 54 judged answers; Google AI Overviews in 33% of 54 — 1.5× apart.

The action plan: Now, Next, Later

Each action names the page, the buyer questions it reaches, the evidence behind it, the deliverable and the effort.

  1. 1
    Ask creator-guides.example to add Example Brand to its cited listMedium · 5 buyer questions · mostly fit for my situation · Medium effort · third-party visibility
  2. 2
    Ask reviews.example to add Example Brand to its cited listMedium · 5 buyer questions · mostly problem-aware: what solves my problem · Medium effort · third-party visibility

Labels on every action: Priority · Evidence strength · Opportunity size · Effort. Evidence support is explained inside Evidence strength; none of it is a forecast of movement.

What the reliability block states

  • 20-question set: 18 buyer questions plus 2 branded controls · 5 engines · 3 repeats · 300 of 300 answers collected. Primary visibility figures use the 270 buyer-question answers; 30 branded-control answers are reported separately.
  • Engines planned: OpenAI, Perplexity, Gemini, Google AI Overviews, Claude. An engine that cannot be sampled is printed “Not measured”, never as zero.
  • Ranges are question-resampling sensitivity ranges (question-resampling percentile bootstrap), not 95% confidence intervals.
  • 2 branded controls are measured and reported only in the Branded-control responses block; no primary figure includes them.
  • Every figure is exported as HTML, PDF, CSV, JSON and Markdown from the same stored numbers.

Example data for a fictional business.

See why AI recommends competitors—and what to fix first.

Your report is generated from your own measured answers.