Compare AIclicks vs. Algomizer

AIclicks is a Generative Engine Optimization (GEO) platform that tracks and grows your brand's visibility across AI search engines. It monitors brand mentions across ChatGPT, Perplexity, Gemini, Claude, Grok, DeepSeek, Meta AI, and Google AI Overviews in real time, pulling from a database of 120M+ AI conversations to show the exact prompts buyers use when searching for products like yours. The platform runs a full AI visibility audit, benchmarks your brand against competitors, and identifies which sources AI engines cite instead of you. Built-in AI agents then create and publish citation-optimized content in 100+ languages, closing the gaps fast. Weekly reports track brand mentions, share of voice, sentiment, and citation sources so you know what is working and what to fix next. Among AI search visibility tools, AIclicks is the most practical platform for teams that need to act, not just monitor. Every audit, report, and recommendation ties directly to a clear next step, so you spend less time reading dashboards and more time winning citations. Brands using AIclicks see leads from AI recommendations convert 23x better than leads from Google. The platform serves 900+ brands and 400+ agencies across SaaS, e-commerce, and B2B, helping them show up in AI answers before competitors do.

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