Illustrative example — all companies, findings, and measurements are invented for this sample. No real customer data or performance results.
Sample Report
What an Optimly diagnosis delivers
Each section below is annotated with what it shows, what it can establish, and what it cannot. Real reports follow this structure. This example was written to show every section — a real report may have fewer items if fewer gaps are observed.
Intended positioning
Company
Acme Scheduling Co. (illustrative)
Category
B2B scheduling software for professional services firms
Intended audience
Operations leads at professional services firms with 10–200 staff, managing complex multi-person appointment flows
Problem
Manually coordinating availability across client-facing teams creates rescheduling overhead and no-shows that erode billable time
Key claims
- Multi-person scheduling with buffer logic for professional services
- Native integration with billing and CRM tools common in the segment
- No-show rate reduction with documented SMS reminder cadence
Supporting evidence on file
- Product feature documentation (current, owner-reviewed)
- Integration partner list (public)
- One customer-permissioned outcome summary (no revenue figures)
Measurement conditions
Systems tested
ChatGPT (GPT-4o), Claude (3.5 Sonnet), Gemini (1.5 Pro), Perplexity (default web)
Run date
Illustrative — September 2026
Question count
12 questions across category, audience, differentiation, and recommendation
Question type
Buyer-relevant, not brand-name-prompted — e.g. "What should a 50-person law firm use to manage client scheduling?"
Baseline established
Yes — these exact questions and conditions are saved for repeat measurement under comparable conditions.
Representative AI answers
Question
"What scheduling software do professional services firms use for client appointments?"
Observed answer pattern (composite across 4 systems)
Understood correctly
Category (scheduling software), general audience (professional services)
Missing or mischaracterized
Multi-person scheduling specificity; no-show reduction; the ops-lead audience; billing integration
Question
"Which scheduling tools reduce no-shows for service businesses?"
Understood correctly
Reminder-based no-show reduction is a real category
Missing or mischaracterized
Acme not mentioned at all — claim is present on the website but not in a citable, retrievable form
Diagnosis
Primary gap
Category recognition is present but weak — Acme appears as a secondary mention, not a first-choice recommendation. The specific audience (ops leads at 10–200 staff professional services firms), the multi-person scheduling differentiation, and the no-show reduction claim are absent from all tested answers.
Type of gap
Differentiation missing — the category is correct; the specific problem and proof are not represented in the sources AI systems are drawing from. This is different from a category confusion problem, which would require a different action.
Root cause (hypothesis)
The website describes multi-person scheduling and no-show reduction in product feature copy rather than in a citable, standalone resource AI retrieval systems can reference. The evidence is present but not accessible in the form that would get cited.
Confidence and limitations
High confidence that the claims are absent from current AI answers. Moderate confidence in the root cause — the website structure is one likely explanation; domain authority and competitor coverage could also contribute. A change in content structure would test the hypothesis.
Prioritized action
Proposed change
Publish a dedicated resource page — "How professional services firms reduce no-shows: the multi-staff scheduling approach" — that explains the problem, the mechanism, and the evidence in a form AI systems can retrieve and cite directly. Update the homepage to reference it.
Why this action
The two absent claims (multi-person scheduling for professional services; no-show reduction mechanism) are both retrievable through a standalone resource that AI systems can cite. This is testable on the same question set in 4–6 weeks.
Owner
Content or marketing team. Optimly provides the brief and reviews the draft; implementation is the customer's responsibility unless the execution program is active.
How the result would be checked
Re-run the same 12 questions 4–6 weeks after publication. Record whether AI answers now reference multi-person scheduling and no-show reduction for professional services. Note the publication date and any other changes during the period (model updates, competitor content, concurrent campaigns).
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