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Solutions for Understanding Why Competitors Get Recommended: 7-Platform AI Consensus Index"},"limitations":["Exactly 7 platforms were included in this run: openai, anthropic, deepseek, grok, perplexity, kimi, google. The configured source value 7 is provenance only and must never be described as the number of platforms studied.","The finalrankingtable is the sole authority for rank, platform mentions, platform share, average rank, and best rank. Ignore any conflicting value embedded in a entity evidence bundle.","The Consensus Ranking table must contain exactly one row per finalrankingtable entity, in that order, using that exact entityname. Never split one ranked entity into two product, model, SKU, or product-line rows, and never add another ranking row for a brand already listed. Put recommended products or plans inside that entity's ranked section.","Platform mentions count only ranking-discovery mentions. Do not substitute the number of platforms that later completed a fit assessment.","Citation IDs inside each entity bundle are local to that entity. Consolidate sources by URL using the supplied source catalog.","The URLs were collected from platform responses and were not independently validated by either writer stage.","Each fitreviewurlorplaceholder is the final site-relative CMS path for that entity's review. Link the entity name to that exact path exactly once in its ranked prose section, outside tables and headings. Never replace it with a run ID, report ID, UUID, or local output path.","Use the exact entity-namespaced citation IDs from the source catalog (e.g. e1:); never transfer a citation between entities."],"methodology":{"fit_rating_contract":["strong","good","mixed","weak","uncertain"],"included_valid_platform_count":7,"maximum_finalists":10,"minimum_mentions":2,"name":"AI Marketing Consensus Index cross-platform AI consensus analysis","platforms_included":["anthropic","deepseek","google","grok","kimi","openai","perplexity"],"platforms_queried":["openai","anthropic","deepseek","grok","perplexity","kimi","google"],"stages":["ranking_discovery","fit_assessment","normalization","editorial_synthesis"],"version":"alias-table-1"},"provenance":{"generated_at":"2026-09-19 14:53:39.775929+00","research_date":"2026-09-19","source_snapshot_sha256":"4ec5238939edc38c35fde149584d65d4cb9995a70c3fbd7b359ccf656eb489e9","verification_scope":"public_verification"},"ranking":{"candidates_evaluated":41,"qualified_finalists":10,"ranking_unit":"Software, service, or advisory solution provider"},"schema_version":4,"summary":"Profound is the consensus leader for understanding why competitors get recommended across AI systems, named by 6 of 7 platforms with an average listed position of 2.5 and a best position of 1. Peec AI is the strongest alternative for teams that need source- and URL-level gap explanations at a transparent self-serve price, while Scrunch leads for citation-level analysis by URL, domain, persona, and funnel stage. Shadow is the pick for communications and PR teams that want managed interpretation and execution rather than raw dashboards. This study covered 7 platforms (openai, anthropic,…","target_buyer":"Company or marketing team seeking AI Visibility Solutions for Understanding Why Competitors Get Recommended","topic":{"base_category":"AI visibility and LLM monitoring platforms","category_criteria":["A solution that can measure recommendation gaps","identify the prompts where competitors win","analyze the citations and source architecture surrounding those answers","benchmark competitors historically","and help identify actionable reasons for the difference"],"geography":"United States","slug":"best-ai-visibility-solutions-for-understanding-why-competitors-get-recommended","title":"Best AI Visibility Solutions for Understanding Why Competitors Get Recommended","use_case":"AI Visibility Solutions for Understanding Why Competitors Get Recommended"}}