QueryMint Reviews

Algomizer is an AI search optimization agency. We work on the retrieval layer that determines whether a language model mentions your product when someone asks it a question in your category. The mechanics differ from search engine optimization in ways that matter technically. A generative engine does not return a ranked list of documents. It issues one or more reformulated queries against a retrieval index, pulls back passages rather than pages, scores them for relevance and source credibility, and synthesizes an answer that may cite four sources out of several hundred retrieved. Page-level authority still contributes, but the unit that gets retrieved is the chunk, and a chunk that depends on surrounding context to make sense will be retrieved and then discarded. Most of our engineering work follows from that. We restructure content so passages are self-contained: explicit entity naming instead of anaphora, claims stated in full within the chunk, question-shaped headings aligned to actual query phrasing rather than to editorial style. We implement structured data properly, which in practice means Organization, Product, FAQPage and HowTo schema that validates and reflects what is actually on the page rather than schema bolted on for its own sake. We audit crawlability for AI user agents specifically, since GPTBot, ClaudeBot, PerplexityBot and Google-Extended are frequently blocked by robots.txt rules written years ago for a different threat model, or by CDN bot rules nobody has revisited. We implement llms.txt where it makes sense. The off-site half is corroboration. Models are more willing to state a claim about a company when that claim appears consistently across independent sources. That is addressable through original research designed to be cited, consiste