Based on what you know about [BRAND] and [CATEGORY], what are the 5 most important topics, questions, or content types that [BRAND] should have strong public coverage on — but currently doesn't?

For each gap: explain why this topic matters for AI recommendation frequency, and what "strong coverage" would look like.

Map the content [BRAND] would need at each stage of the [TARGET AUDIENCE] buyer journey to maximize AI recommendation frequency:

Stage 1 — Problem awareness: What questions does [TARGET AUDIENCE] ask when they first realize they have the problem [BRAND] solves? Does [BRAND] have strong, specific content that addresses these questions?

Stage 2 — Solution exploration: What questions does the buyer ask when they start researching solutions in [CATEGORY]? Is [BRAND] present in those conversations?

Stage 3 — Comparison and evaluation: What are the specific comparison queries a buyer makes — "[BRAND] vs [COMPETITOR]", "best [CATEGORY] for [SPECIFIC CONTEXT]"? Does [BRAND] have content that earns retrieval here?

Stage 4 — Validation: What does a buyer look for to confirm they're making a safe, smart choice? Testimonials, case studies, third-party reviews, analyst coverage — where are [BRAND]'s gaps?

For each stage: rate [BRAND]'s current coverage (strong / moderate / weak / absent) and name the single highest-impact content investment for each weak stage.

Generate the 15 most important questions a [TARGET AUDIENCE] asks before making a purchase decision in [CATEGORY].

For each question:
1. State the question as a buyer would actually phrase it
2. Rate how well [BRAND]'s current public presence would allow you to answer it confidently on behalf of [BRAND] (1 = nothing, 5 = strong, specific signal)
3. Name the specific content type that would fill the gap if the score is below 3

After all 15: which three gaps are most damaging to [BRAND]'s recommendation frequency? Rank them by impact, not by how easy they are to fill.

Comparison queries are some of the highest-intent moments in the buyer journey — and AI recommendations in these moments heavily influence decisions.

Audit [BRAND]'s content coverage for the most important comparison contexts:

1. "[BRAND] vs [COMPETITOR]" — does sufficient public content exist for you to give a confident, specific comparison?
2. "[COMPETITOR] alternatives" — would [BRAND] appear when someone is looking for an alternative to [COMPETITOR]?
3. "Best [CATEGORY] for [SPECIFIC AUDIENCE/USE CASE]" — does [BRAND] have enough specific positioning to appear in niche comparison queries?
4. "[BRAND] review" or "[BRAND] is it worth it" — is the review and social proof landscape strong enough for a positive sentiment signal?

For each: rate the current coverage and describe the content that would strengthen it most.

Proof content — case studies, outcome data, customer testimonials, third-party validation — is the most credibility-dense content type for AI retrieval.

Audit [BRAND]'s proof content gaps:

1. Specificity gap: Are [BRAND]'s case studies and testimonials specific enough — named customers, concrete numbers, clear context — to function as credible signals? Or are they generic enough to be dismissed?

2. Industry coverage gap: Does [BRAND] have proof from the specific industries and company types it targets? Or does its evidence come from a different segment than its ideal buyer?

3. Use case coverage gap: Does [BRAND] have proof for each of its primary use cases — or is evidence concentrated in one application area?

4. Recency gap: Is [BRAND]'s proof archive current — with recent customers, recent outcomes, recent validation — or is it aging in a way that signals stagnation?

5. Third-party gap: Is there independent validation of [BRAND]'s claims — analyst reports, award listings, independent reviews, press coverage — or is all proof self-generated?

For each gap: high / medium / low priority, and the specific content action that would close it.

Original research is the highest-leverage content investment for building AI recommendation presence. When a brand publishes proprietary data, it becomes the primary source — and AI systems cite primary sources.

Evaluate [BRAND]'s opportunity to build an original research content moat:

1. What data does [BRAND] have access to — from its product, its customer base, its industry experience — that no one else has?

2. What questions are [TARGET AUDIENCE] asking in [CATEGORY] that no one has published comprehensive data on? These are the research gaps [BRAND] could own.

3. What format — annual benchmark report, quarterly data pulse, survey-based research, proprietary index — would have the most strategic value for [BRAND]'s audience and category position?

4. What would it take to publish the first edition of that research — in terms of data collection, methodology, and distribution — and what would "success" look like at 12 months?

Based on the content gap analysis for [BRAND] in [CATEGORY], build a 90-day content sprint designed specifically to improve AI recommendation frequency.

Assume:
- One content strategist
- One writer
- Budget for 2 pieces of original research or production-heavy content
- Publishing cadence of 2–3 pieces per week

Structure the sprint in three phases:

Phase 1 (Days 1–30) — Foundation: Which existing content needs to be updated, expanded, or restructured to earn stronger retrieval signals? What quick wins are available from existing assets?

Phase 2 (Days 31–60) — Gap filling: Which of the highest-priority content gaps (from the audit) should be addressed with new content? What formats, topics, and angles should each piece take?

Phase 3 (Days 61–90) — Authority building: What longer-form, original, or research-led content should be produced to start building a compounding signal that improves recommendation frequency over the following 6–12 months?

Be specific: name actual topics, formats, and proof requirements — not just content categories.