HONEST ALTERNATIVE · SEO + AI VISIBILITY
InLinks is an entity SEO and internal linking automation platform that uses NLP to identify entities in content, map them to a site-wide topic wheel, automate internal linking between topically related pages, and generate JSON-LD structured data. DeepSEOAnalysis validates the technical implementation quality of structured data: property-level JSON-LD validation, server-rendering detection (critical for InLinks-generated JavaScript-injected schema), real CrUX p75 from Chrome users, and five-signal AI visibility scoring. Entity optimisation generation vs technical quality validation — complementary tools for entity-based SEO programmes.
At a glance
The useful answer is not “which tool is best?” It is which tool fits the job you need done this week.
| Criterion | Competitor | DeepSEOAnalysis |
|---|---|---|
| Core function | InLinks is an entity SEO and internal linking automation platform. It uses NLP to analyse content, identify entities (topics, people, places, organisations, products), map those entities to a site-wide "topic wheel", automatically suggest and implement internal links between topically related pages, and generate entity-based JSON-LD schema markup. InLinks positions itself at the intersection of on-page content optimisation and structured data automation — using entity recognition to both improve topical authority signals (internal linking) and express entity relationships in schema. | A per-URL technical SEO and AI visibility auditor. Submit any URL for an on-demand audit: property-level JSON-LD structured data validation (including server-rendering detection), real CrUX field data (p75 LCP, CLS, INP from Chrome users), canonical tag correctness, robots.txt and AI crawler access, llms.txt presence, broken outbound links, and five-signal AI visibility scoring. Validates the technical implementation quality of structured data rather than automating its generation. |
| Entity SEO and NLP analysis | InLinks uses NLP to identify entities in content and map them to a site-wide topic wheel — a visual representation of which entities the site covers and how they relate to each other. The entity analysis powers two core features: (1) Internal linking: InLinks identifies which pages on the site are topically related via shared entities and automatically suggests (or inserts) internal links between them — building topical authority by connecting entity-related content across the site. (2) Schema generation: InLinks generates JSON-LD structured data based on entity recognition — creating Article schema with entity references, Topic schema for hub pages, and structured data expressions of the entity relationships it identifies. | No entity recognition or NLP-based content analysis. DeepSEOAnalysis validates what structured data is already present on the page rather than generating new schema from content analysis. For the entity SEO pipeline — identifying which entities to build content around and automatically linking entity-related content — InLinks addresses this generation phase; DeepSEOAnalysis addresses the validation phase: confirming that what InLinks generated is correctly server-rendered and technically valid. |
| Internal linking automation | InLinks's internal linking feature analyses the entity profile of each indexed page and identifies contextually relevant linking opportunities across the site. It can automatically insert internal links (via JavaScript injection into page content, or by integrating with CMS plugins for WordPress) or suggest link opportunities for manual implementation. The entity-based approach means links are inserted where the target page's primary entities match the source page's content context — creating semantically appropriate internal link anchor text and placement. | No internal linking analysis or automation. DeepSEOAnalysis detects broken outbound links on the audited page (both internal and external links) and validates canonical configuration. For internal linking strategy and implementation at scale — identifying which pages should link to each other and what anchor text to use — InLinks addresses this; DeepSEOAnalysis validates page-level technical quality, including whether canonicals are correctly configured on pages that receive internal links. |
| Structured data validation vs generation | InLinks generates schema markup based on entity recognition — producing Article schema, Topic schema, and entity relationship structured data. The schema is typically deployed via JavaScript injection (similar to WordLift's approach) rather than server-rendered HTML. This means InLinks-generated schema may be JavaScript-injected rather than present in the initial HTML response — creating the server-rendering gap that affects rich result eligibility and AI search citation. | Property-level JSON-LD validation with explicit server-rendering detection. The audit checks whether structured data (including InLinks-generated schema) is in the initial server-rendered HTML or only available after JavaScript execution. This server-rendering check is particularly important for InLinks users because InLinks typically deploys schema via JavaScript injection — and JavaScript-injected schema has the same server-rendering eligibility risks as any client-side schema. DeepSEOAnalysis is the right tool to confirm whether InLinks-generated schema is being processed by Googlebot. |
| Core Web Vitals and performance | InLinks does not measure Core Web Vitals or page performance. For deployments where InLinks injects internal links and schema via JavaScript, the JavaScript overhead of the InLinks script on the host page is a CWV consideration — particularly for INP on pages where InLinks JavaScript runs content modification logic after initial page load. | Real CrUX field data at p75 from Chrome users for every audited URL: LCP, CLS, and INP status. For pages where InLinks JavaScript is embedded, the CrUX audit reflects the actual performance impact of the InLinks script in real user conditions. High INP on pages with InLinks script overhead would be identified and flagged. |
| AI visibility and GEO scoring | InLinks's entity-based content optimisation has conceptual alignment with AI search visibility — AI systems use entity graphs and semantic relationships to identify authoritative content. However, InLinks does not include explicit AI visibility scoring, AI crawler access checking, llms.txt support, or the five-signal GEO audit framework. | Five-signal AI visibility scoring: AI crawler access in robots.txt (GPTBot, ClaudeBot, PerplexityBot), llms.txt presence, FAQPage/HowTo JSON-LD server-rendering, question-heading ratio ≥20%, content chunkability ≤400 words average. InLinks's entity optimisation builds topical authority; DeepSEOAnalysis confirms whether AI crawlers can access and index the entity-optimised content. |
| Pricing and access model | InLinks pricing starts at approximately $49/mo for small sites (up to 7 web pages, 35 entity briefs) to $599/mo for larger sites (200 pages, 1,000 entity briefs). Annual plans available at discount. All plans include entity analysis, internal linking automation, and schema generation. A free trial allows limited testing before commitment. | Anonymous full technical audit: $0, no signup, no email gate — the complete report at no cost. Free account: saved reports and one monitored site. Pro is $24/mo for five monitored sites; Agency is $89/mo for 25 sites and white-label workflows. |
What each product is
InLinks uses NLP entity recognition to build a site-wide topic wheel, automate internal linking between entity-related pages, and generate Article and Topic JSON-LD structured data — scaling entity-based on-page optimisation from approximately $49/mo to $599/mo depending on site size.
DeepSEOAnalysis audits individual pages for technical SEO quality: property-level JSON-LD validation with server-rendering detection (confirming whether InLinks-generated schema is in initial HTML or JavaScript-injected), real CrUX p75 from Chrome users, five-signal AI visibility scoring, and broken link detection — free with no signup required.
When to choose which
Pricing context
FAQ
InLinks is used for entity SEO and internal linking automation. It analyses content using NLP to identify entities (topics, people, places, organisations), maps those entities to a site-wide topic wheel, automatically suggests or implements internal links between topically related pages, and generates JSON-LD structured data based on entity recognition. It is primarily used by SEO teams and agencies that want to scale entity-based optimisation and internal linking without manually identifying and implementing every link and schema element. InLinks is particularly relevant for content-heavy sites (blogs, news sites, content marketplaces) where manual entity mapping and internal linking at scale would be prohibitively time-consuming.
InLinks typically deploys schema via JavaScript injection — inserting JSON-LD into the page DOM after the initial HTML is served, rather than including it in the server-rendered HTML. This JavaScript injection approach creates a server-rendering gap: Google's HTML parser processes the initial HTML before JavaScript execution, so JavaScript-injected schema may not be processed for all rich result types and AI search citation. The risk is the same as with WordLift, Alli AI, and other JavaScript schema deployment approaches. DeepSEOAnalysis explicitly checks whether structured data is in the initial HTML response or JavaScript-injected — the most important validation for InLinks users to confirm their schema is being processed correctly.
InLinks and WordLift are both entity SEO platforms that use NLP to identify entities and generate structured data, but they approach this differently. WordLift focuses on semantic enrichment — connecting entities to Wikidata and DBpedia, building a linked open data graph, and generating entity-enriched schema. InLinks focuses more on internal linking automation and topic authority building — using entity recognition primarily to identify which pages should link to each other and to generate schema that expresses topical relationships. Both deploy schema via JavaScript in their default configurations, creating the same server-rendering gap. Both benefit from DeepSEOAnalysis validation to confirm their generated schema is server-rendered and technically correct.
Yes — they address complementary phases of entity SEO. InLinks identifies entities in content, builds the topic wheel, automates internal linking, and generates schema markup (the content analysis and generation phase). DeepSEOAnalysis validates the technical implementation: confirming that InLinks-generated schema is server-rendered in the initial HTML (not JavaScript-injected only), validating required schema properties are present, checking CrUX p75 performance impact of InLinks JavaScript on host pages, and confirming AI crawler access is enabled on optimised pages. Running DeepSEOAnalysis on key pages after InLinks deployment confirms the schema is technically correct and eligible for rich results and AI citation.
Paste a URL and get the complete technical SEO, Core Web Vitals, structured data, internal linking, CMS, and AI-visibility audit.
Run a free audit →