Compare AEO Armory vs. Algomizer

AEO Armory serves as a web-based optimization tool designed for answer engine optimization (AEO) and generative engine optimization (GEO), helping businesses evaluate their presence in AI-driven searches. By inputting their business name and website details, users can generate reports that leverage ChatGPT and Google AI Mode with prompts tailored for customer engagement, allowing them to analyze factors such as business recognition, ranking, competitive landscape, and referenced websites. The tool assesses the website's preparedness for citation, evaluating aspects like crawler accessibility, structured data, content quality, and trust indicators across a maximum of 15 pages. Comprehensive reports feature an AI visibility score, insights specific to each page, prioritized suggestions with recommended text, a strategic prompt plan, a checker for fixes, and an off-site engagement strategy. Users have the capability to monitor their reports and implemented changes through an accessible dashboard, with the option to download reports in PDF format. The free version of AEO Armory allows for the generation of up to three reports daily and provides access to seven additional AEO tools. In addition to analyzing public web pages and delivering actionable insights, users are responsible for implementing the recommended adjustments to their sites themselves, fostering a hands-on approach to enhancing their online visibility.

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