TEARDOWN 46 · REVIEWED 2026-08-21
InLinks
InLinks is an SEO platform that crawls a website, then automatically inserts internal links between related pages, adds schema markup, and groups content into topic clusters based on the entities (people, places, concepts) it detects in the text. Every paid plan also includes an AI writing assistant and keyword research.
InLinks crawls a customer's own site and matches pages to shared topics and entities to auto-insert links and schema — real integration work an AI coding tool can approximate with an open-source text-processing library and a free entity-lookup API, but its tuned topic-clustering isn't a weekend rebuild.
What you lose building it yourself
- ✕Entity-matching engine tuned across 23,000+ customer accounts (vendor figure)
- ✕One-click schema markup across multiple content types, kept current as Google's requirements change
- ✕Topic Planner's content-gap analysis across an entire site
- ✕Ongoing maintenance as Google's entity and schema rules shift
Why people still pay
The subscription buys a topic/entity-matching engine already tuned across tens of thousands of sites, plus schema markup kept current as Google's requirements change — not something a one-off script replicates.
BUILD YOUR OWN · WEEKEND-PLUS SCOPE (SITE-LEVEL ENTITY LINKING)
A basic version — crawl your own site, pull out repeated topics/entities with an open-source text-processing library, and auto-suggest links between pages that share them — is realistic for an AI coding tool to build over a few sessions, not an afternoon. Matching InLinks' tuned matching engine, its content-gap topic planner, and automatic multi-type schema markup takes real, ongoing work most solo builds won't reach.
StackA site crawler (Python + httpx or Scrapy) to pull your own pages · an open-source NLP library (spaCy) for entity/keyword extraction · a free entity-lookup API (Google Knowledge Graph Search API) to resolve entities · a matching script to compare extracted entities across pages and flag link candidates · JSON-LD templates for common schema types.
Build a Python script using httpx and BeautifulSoup that crawls up to 100 pages of a single site (given a starting URL, staying on the same domain), extracts the page title, meta description, and H1/H2 headings from each page, and uses spaCy's built-in named-entity recognizer to pull out the 5 most frequent entities (people, organizations, concepts) per page. Build a simple co-occurrence table showing which pages share the same top entities, and output a CSV listing suggested internal link pairs — source page, target page, and the shared entity as suggested anchor text — for any pair sharing 2 or more entities. Cap the crawl at 100 pages and rate-limit requests to 1 per second.
QUESTIONS
Does InLinks crawl the whole web, or just my own site?
Just the customer's own site — it builds its topic/entity map from the pages you point it at, not a pre-built web-scale index the way a backlink tool would.
Is there a free plan?
No free tier of the core product — plans start at $49/month for a Freelancer account covering 100 pages. A few separate single-purpose free tools exist on the site, but they aren't the internal-linking/schema engine itself.
Is InLinks tied to WordPress like a plugin?
No — it works through a tracking tag or API rather than being built into one CMS, unlike a WordPress-specific internal-linking plugin.