The AI search category (Perplexity, You.com, Phind, Andi, Kagi, Brave Search AI) is split by three concrete dimensions: which foundation models power the answers, how citations get rendered and verified, and what indexing scope the engine pulls from. Hand-written review pages drift because every engine ships model changes monthly and the citation handling, the part that matters most for serious users, gets reframed quarterly as engines refine their approaches.

SleekRank reads a sheet of AI search engines with name, pricing model, underlying foundation models as a JSON array, citation handling depth, indexing scope (web, academic, code, custom corpus), target audience (researchers, developers, general), and a verdict. The base WordPress page is a standard comparison: hero, pricing card, models grid, citation depth pill, indexing scope, verdict block, FAQ. List mappings render the models grid and indexing scope, selector mapping fills the verdict and audience pills.

A second page group keyed on a pairs sheet generates /ai-search/{a}-vs-{b}/ pages joining both engine rows for side-by-side comparison. Pricing and model updates flow from one cell to per-engine and pair pages after the cache cycle. Sitemap auto-includes generated URLs, base page is noindexed, removed rows stop generating URLs as engines launch and discontinue.

The AI search engine category moves on a model-release cadence that no editorial team can match by hand. Perplexity might swap from GPT-4 to GPT-4o between two consecutive review-cycle dates, You.com might add a new mode, Phind might fine-tune a new in-house model, and the existing review pages on most publishing sites will be wrong for weeks before anyone notices. Citation handling, the dimension that most distinguishes a serious AI search engine for research use from a chat product with web results, gets reframed in vendor marketing every quarter, and review pages need to track those reframings carefully because they are exactly the spec readers come to compare.

Manual reviews on WordPress drift on models and citation depth simultaneously, producing a comparison ecosystem where readers learn to distrust the category coverage because facts contradict the vendor's own current docs. SleekRank addresses this structurally. Every page rendering Perplexity's underlying models reads from the same row, and one edit propagates to per-engine and pair pages.

The citation depth column updates the same way. For a tech publication or research-focused affiliate site, this is the difference between a corpus that decays in accuracy weekly and a database-driven resource where model lists and citation specs stay in step with the engines as they ship.