Feature Gap Deep Dive (Curated Pass)

This page is the second-pass, manually curated analysis on top of the full inventory. It focuses on product capabilities that materially affect win-rate, retention, and enterprise deal velocity for Visyble.

All recommendations here are derived from the full processed corpus (175/175 pages); see competitive-intelligence/coverage-audit-and-traceability for verification.

How to read this page

• What competitors do: concise pattern summary with direct references.
• Visyble current state: what exists based on current internal docs.
• Gap: what is missing or under-exposed.
• Build spec: concrete delivery guidance (data model, API, UI, KPIs).

1) Prompt Diagnostics: Fanouts, Intent, and Query Paths

What competitors do

• Profound strongly productizes prompt intelligence beyond “volume”: fanouts, intent, and prompt recommendations.
• Evidence:
  • Introducing Query Fanouts
  • Expanding analysis for query fanouts
  • Introducing Conversation Intent in Prompt Volumes
  • Introducing Prompt Recommendation Engine
  • Prompt Volumes (product launch)

Visyble current state

• Prompt Volumes exists as a section in docs and product taxonomy.
• No first-class docs flow for fanout tree generation, intent tagging model, or query transformation lineage.

Gap

• Insight depth is lower than competitor narrative: Visyble can show prompt activity, but not enough “why/how the model decomposed this query”.

Build spec

• Data model
  • prompt_runs -> fanout_queries[] (query text, rank, source engine, timestamp, locale).
  • intent_labels[] with confidence and optional multi-label tags.
• API
  • GET /prompt-diagnostics/{prompt_id}
  • GET /prompt-diagnostics/{prompt_id}/fanouts
  • GET /prompt-diagnostics/{prompt_id}/intent
• UI
  • Fanout graph panel + expandable query table.
  • Intent chips with confidence and trend-over-time.
  • Source overlap panel by fanout branch.
• KPIs
  • Fanout breadth, intent drift rate, fanout-to-citation conversion rate.

2) Citation Intelligence: Categories, Gaps, and Benchmarks

What competitors do

• Both Peec and Profound emphasize citations/sources as the optimization control surface.
• Evidence:
  • Brand mention gap analysis (Peec)
  • Source gap analysis (Peec)
  • Citation rate benchmarks (Peec)
  • Enhanced citation categories (Profound)
  • Citation overlap strategy (Profound)
  • How ChatGPT sources the web (Profound)

Visyble current state

• Sources exists and includes URL analytics endpoint in generated docs.
• No clear docs taxonomy for source classes (news/docs/forums/reviews/ugc) and no published benchmark ranges.

Gap

• Missing “decision layer” for users: which source category matters most for each objective and model.

Build spec

• Data model
  • source_category, source_trust_score, source_type, model_overlap.
  • Gap objects: mention_gap, source_gap, citation_gap.
• API
  • GET /sources/categories
  • GET /sources/gaps
  • GET /benchmarks/citation-rate
• UI
  • Category treemap + benchmark gauge.
  • Gap explorer with suggested actions.
• KPIs
  • Citation share by category, benchmark percentile, gap closure velocity.

3) Asset Hierarchies and Segmentation

What competitors do

• Profound highlights hierarchy and segmentation (asset/persona/demographic) as core analysis dimensions.
• Evidence:
  • Introducing Asset Hierarchies
  • Introducing Demographic Information in Prompt Volumes
  • Personas feature

Visyble current state

• Brands, Knowledge Bases, and Personas APIs exist.
• No documented hierarchy rollups from brand -> product -> feature -> URL/campaign.

Gap

• Multi-product customers cannot easily connect visibility shifts to sub-assets with clean rollups.

Build spec

• Data model
  • entity_nodes (brand/product/feature/page), entity_edges, rollup_rules.
• API
  • POST /entities
  • POST /entities/edges
  • GET /entities/{id}/rollup
• UI
  • Hierarchy builder and rollup filters on all analytics screens.
• KPIs
  • Sub-asset visibility trend, rollup accuracy, segment-level contribution to brand score.

4) Content Optimization Workbench + Action Loop

What competitors do

• Profound repeatedly ships optimization workflows and content scoring patterns.
• Evidence:
  • Introducing content optimization
  • AEO content score
  • Analyze any content, anywhere
  • Markdown vs HTML experiment
  • Profound Workflows

Visyble current state

• Actions, Suggestions, and Agents exist.
• Generated docs show Content section without discovered API flow.

Gap

• Missing closed loop from insight -> content recommendation -> execution -> measured lift.

Build spec

• Data model
  • content_assets, content_score, recommendation_cards, execution_jobs.
• API
  • POST /content/analyze
  • GET /content/{id}/score
  • POST /content/{id}/apply-suggestion
• UI
  • Content workbench with scoring breakdown and “run action” controls.
• KPIs
  • Score lift after actions, citation lift by content cohort, time-to-impact.

5) Agent Analytics Integrations (CDN/CMS/Storefront)

What competitors do

• Profound invests heavily in connector breadth and ecosystem lock-in.
• Evidence:
  • Akamai integration
  • GCP integration
  • WordPress integration
  • Shopify support
  • Slack + CMS operational integrations
  • Contentful integration

Visyble current state

• Agent Analytics exists with Cloudflare and related status endpoints.
• Integration story is present but less explicit as a broad connector strategy in docs UX.

Gap

• Perceived platform completeness is lower for buyers who compare “integration matrix” first.

Build spec

• Data model
  • integration_instances, connector_capabilities, sync_status, audit_logs.
• API
  • GET /integrations/catalog (already present) -> expand metadata for ecosystem coverage page.
  • POST /integrations/{connector}/onboard.
• UI
  • Integration matrix page (supported, beta, roadmap).
  • Setup wizard per connector.
• KPIs
  • Connector activation rate, time-to-first-signal, data freshness SLA.

6) Commerce Intelligence Package

What competitors do

• Competitors publish dedicated shopping intelligence and commerce-specific benchmarks.
• Evidence:
  • ChatGPT Shopping support
  • Shopping analysis
  • 2M prompts shopping behavior
  • Black Friday shopping index
  • Universal Commerce Protocol (Peec)

Visyble current state

• No explicit commerce vertical package surfaced in current docs IA.

Gap

• Missing a high-intent GTM package for ecommerce and retail buyers.

Build spec

• Data model
  • shopping_prompts, merchant_mentions, product_recommendation_slots, price_context.
• API
  • GET /commerce/overview
  • GET /commerce/prompts
  • GET /commerce/brands/{id}/share
• UI
  • Commerce dashboard with recommendation share and seasonal event views.
• KPIs
  • Shopping visibility share, product recommendation rate, seasonal performance index.

7) Trust, Compliance, and Enterprise Readiness

What competitors do

• Trust assets are used as product marketing and enterprise procurement enablers.
• Evidence:
  • SOC 2 Type 2
  • HIPAA compliance
  • Enterprise features narrative

Visyble current state

• Billing/settings/system controls exist; trust posture is not prominently structured in docs navigation.

Gap

• Enterprise buyers may miss critical trust signals during evaluation.

Build spec

• Docs IA
  • Add Trust Center section under System.
• Content
  • Security controls matrix, data retention policy, compliance roadmap.
• Product hooks
  • SSO/SCIM, audit exports, role templates documented alongside controls.

8) Benchmark Program and Category Authority

What competitors do

• Both competitors continuously publish benchmark-style content tied to product capabilities.
• Evidence:
  • Citation rate benchmarks (Peec)
  • Self-promotional listicles analysis (Peec)
  • AI platform citation patterns (Profound)
  • AI search volatility research (Profound)
  • Research Hub landing

Visyble current state

• Strong internal engineering docs, limited outward benchmark narrative structure.

Gap

• Lower category authority compared to competitors that repeatedly publish data-backed studies.

Build spec

• Quarterly benchmark release process.
• Public methodology page + reproducibility notes.
• In-product benchmark comparison cards connected to customer account metrics.

Recommended release sequence (execution-ready)

1. Sprint 1-2: Prompt Diagnostics + Citation Intelligence v2 foundations.
2. Sprint 3-4: Content Workbench MVP + Asset Hierarchy schema.
3. Sprint 5-6: Integration Matrix + Commerce package alpha.
4. Sprint 7-8: Trust Center + Benchmark publication workflow.