My AI SEO Tools
These are the tools I use on live client projects to measure, fix and grow visibility in Google and AI search. Every card explains how the tool fits my workflow, so agency owners, small businesses and store owners can see the method behind the results.
1. AI Answer Engines
This is where your buyers now ask their questions. I test real customer prompts in each engine to see which brands get named, cited or skipped.
ChatGPT | Prompt & Citation Testing
ChatGPT is where many buyers now start their research. I run the prompts your customers actually type, record which brands and pages it cites with search on, and compare the answer against your site. It also helps me draft briefs and outlines, which I then edit by hand.
Gemini | Google's AI Model
Gemini is the model family behind Google’s AI features, so I test it to understand how Google reads a topic and its entities. When Gemini explains your niche without mentioning your brand, that tells me which entity signals and content gaps to fix on the site. For local businesses, I also check how Gemini describes them next to nearby competitors.
Claude | Research & Automation Partner
I use Claude for deep analysis of long documents, SERP exports and site content, and to write and debug the Python scripts behind my audits. Through MCP it connects to live SEO data sources, so one conversation can pull data, reason about it and produce a clear action list for a client. It is also my second opinion on every audit finding.
Perplexity | Source-Cited AI Search
Perplexity shows its sources for every answer, which makes it the clearest window into how AI search picks pages. I check which domains it cites for your money prompts, study what those pages have in common, and use the pattern to shape your content, structure and internal links. Perplexity often cites smaller sites that answer a question directly, which gives newer brands a real chance.
3. AI Visibility Tracking
Manual checks show the problem. Tracking tools show the trend: how often AI engines mention you versus your competitors, prompt by prompt
DataForSEO | SERP & LLM Mention Data
DataForSEO is the data layer of my AI visibility audits. Its APIs give me Google SERPs with AI Overview references, LLM mention data across AI engines, keyword metrics and backlink data. I pull it with Python and the MCP connector, so every audit is built on raw data, not screenshots. For agencies, I can run the same pipeline on several client sites at once and deliver white-label reports that show exactly which prompts mention each brand and which don’t.
Semrush AI Toolkit | AI Visibility Score
The Semrush AI Toolkit tracks brand mentions, sentiment and share of voice across AI platforms. I use it as a second source next to my own data, so a client never has to rely on one tool’s numbers, and to show how AI engines describe the brand, not just whether they mention it. For e-commerce clients, sentiment matters as much as visibility, because an AI answer that mentions your store with a negative note can cost sales instead of winning them.
Ahrefs Brand Radar | AI Share of Voice
Brand Radar shows how often a brand is mentioned in AI answers compared with its competitors. I use it to benchmark share of voice before work starts, to spot the topics where a competitor owns the AI answer, and to show clients a clear before and after once changes go live. Because Brand Radar covers a large prompt set, it also surfaces questions in your niche that you had not thought to track, which often become new pages in the topical map.
Otterly.AI | Prompt Monitoring
Otterly.AI monitors a fixed set of prompts over time across ChatGPT, Perplexity and Google AI Overviews. I load each client’s money prompts into it after an audit, then track weekly whether their brand appears, which pages get cited and when a competitor takes the spot back. Monitoring matters because AI answers change often. A citation won in one month can be lost the next, and weekly tracking lets me react before traffic and leads drop, not after the client notices.
3. SEO Suites & Search Data
AI engines lean heavily on classic search results, so strong Google and Bing rankings remain the foundation of AI visibility.
Ahrefs | Backlinks & Competitor Research
Ahrefs is my first stop for competitor research. I use it to map which pages earn links and traffic in a niche, find the content gaps between a client and the sites AI engines cite, and audit backlink profiles. Its keyword data also helps me size each topic cluster before planning content. Before any outreach or digital PR work, Ahrefs shows me which sites already link to competitors and which of those links a client could realistically earn, so link building effort goes to the places that move rankings.
Google Search Console | First-Party Data
Search Console is the only source of truth for how Google sees a site. I use it to find queries with impressions but no clicks, pages that are crawled but not indexed, and topics where the site already has partial authority, which are usually the fastest wins. I also compare Search Console data before and after every change, so clients can see what my work did in Google’s own numbers rather than in third-party estimates. It is the report I trust most, and the one I check first every week.
Semrush | Keyword & Position Tracking
I use Semrush for keyword research, position tracking and site audits, especially for local and e-commerce clients who need rankings tracked by city or product category. Its keyword gap and topic research tools also feed the first draft of a topical map, which I then refine by hand using Koray’s framework. For agencies that resell my work, Semrush reports are familiar to their clients, which makes the progress easy to share. I also use its site audit as a quick health check before a deeper Screaming Frog crawl.
Bing Webmaster Tools | Bing & Copilot Data
4. Technical SEO & AI Crawlability
If AI crawlers can’t fetch, render and understand your pages, nothing else matters. These tools find what blocks them first.
Screaming Frog | Site Crawler
Screaming Frog is my core crawler for technical audits. I use it to find broken links, redirect chains, orphan pages, thin content and missing structured data, and I run custom extraction to check how headings and entities are used across hundreds of pages at once. It also helps me test how a site renders JavaScript. For e-commerce stores with thousands of product and filter URLs, it shows exactly where crawl budget is wasted and which pages need to be merged, noindexed or linked better.
PageSpeed Insights | Core Web Vitals
PageSpeed Insights shows real-user Core Web Vitals and lab diagnostics for any URL. I use it to find what slows down key templates, such as product pages and service pages, then prioritise fixes by impact. Faster pages are easier to crawl, easier to render and better for the buyers who land on them. For store owners, I focus on product and collection templates first, because a fix there improves hundreds of pages at once. Every change is re-tested so the client sees the before and after.
Schema Validator | Structured Data
I use the Schema.org validator and Google’s Rich Results Test to check every piece of structured data I add. Clean Organization, Person, Product, LocalBusiness and FAQ markup makes your entities explicit, which helps both Google and AI systems connect your brand to the right topics and attributes. For e-commerce stores, correct Product, Offer and Review markup can also win richer results in Google and clearer product details in AI shopping answers, so I validate every template, not just a sample page.
Cloudflare | Bot & AI Crawler Control
Many sites block AI crawlers by accident through firewall or bot settings. I use Cloudflare’s analytics and bot controls to see which crawlers, such as GPTBot and PerplexityBot, reach a site, and to make sure the ones that drive visibility are allowed while abusive scrapers stay blocked. I also review robots.txt and add an llms.txt file where it helps. Getting crawler access right is often the quickest AI visibility fix of the whole audit, and it costs nothing to implement.
5. Entity SEO & Knowledge Graph
Search engines and LLMs understand entities, not just keywords. These tools show me how machines see your brand and your topic.
Knowledge Graph API | Entity Lookup
Google’s Knowledge Graph Search API tells me whether a brand, person or product exists as a recognised entity. I use it to check a client’s entity status and see how competitors are classified, then build the signals needed to get recognised. For personal brands and consultants, a recognised Person entity is one of the strongest trust signals in both Google and AI answers, and it is where many entity SEO projects begin.
Wikidata | Open Entity Database
Wikidata is one of the open sources that knowledge graphs and LLMs learn from. I use it to research how entities in a niche are linked, and to find the attributes that should appear on a page. I also use it to connect a client’s brand to its founder, location and industry through sameAs links in schema, which strengthens how the brand is understood across the web.
Natural Language API | Entity Analysis
Google Cloud’s Natural Language API extracts entities, salience and categories from any text. I run client and competitor pages through it to see which entities Google is likely to detect, whether the main topic is actually the most salient one, and which related entities are missing from the page. This turns a vague question like whether a page is about the right thing into data. I use the results to rewrite introductions, headings and anchor text so the central entity is clear from the first sentence, for search engines and for readers.
InfraNodus | Knowledge Graph Analysis
InfraNodus turns text into a network graph of concepts and shows the gaps between topic clusters. I use it to analyse a niche’s top content, find the connections competitors miss, and export the graph into Obsidian or Gephi, where it becomes the base of a topical map and an ontology for the client’s site. This is where Koray’s idea of a source context becomes practical: the graph shows which topics the site should own, which it should only touch, and which it should leave to other sites so authority stays focused.
6. Topical Mapping
Topical authority comes from covering a subject completely. These tools help me build
Koray-style topical maps for clients..
Google Autosuggest & PAA | Query Mining
Google’s autocomplete and People Also Ask boxes show the questions real users ask around a topic. I mine them at scale to find the sub-topics, attributes and question formats Google already connects to a central entity. This raw query data is the starting point for every topical map I build, before any paid tool is opened. For local businesses I add city and service modifiers, and for stores I add product attributes such as size, material and use case. The result is a query list that mirrors how real buyers search, not how tools guess they search.
Obsidian | Topical Map & Ontology
Obsidian is where my topical maps live. Each entity, attribute and page becomes a linked note, so I can see the whole structure of a site before writing anything. The graph view makes it easy to spot orphan topics, weak clusters and missing internal links, and the notes turn directly into content briefs for writers. Because every note links to its neighbours, the map doubles as an internal linking plan. Writers know which pages to link and with what anchor text, and the site’s structure stays consistent as new content is added over months and years.
AnswerThePublic | Question Research
AnswerThePublic groups autocomplete data into questions, prepositions and comparisons around a seed term. I use it to see the full range of search intents in a topic quickly, then sort those intents into the core and outer sections of a topical map, so each page answers one clear intent and links to its neighbours. It is especially useful for small businesses entering a new niche, because it shows in minutes which questions a competitor has answered and which remain open. Those open questions often become the pages that AI engines cite first.
Gephi | Network Visualisation
Gephi visualises large networks. I load knowledge graph exports and internal link data into it to see which pages act as hubs, which clusters are isolated, and where link equity pools. For agency clients, a Gephi map is also a clear way to show why a new content structure will build topical authority faster. I also use it to compare a client’s current internal link graph with the planned topical map. The difference shows exactly which links need to be added or removed, turning a complex audit finding into a short, practical task list.
7. Keyword Research & Clustering
Keywords still matter as signals of real buyer demand. These tools help me size that demand
and group every query by search intent.
Keyword Chef | Low-Competition Keywords
Keyword Chef finds long-tail keywords where weak sites already rank, which makes it useful for new sites and small businesses. I use its filters to find realistic targets inside a topical map, so a client can start earning traffic and authority early while bigger competitive pages are still being built and linked. For e-commerce stores, it also finds buying-guide and comparison topics that bring shoppers to category pages. Every keyword I take from it still goes through the topical map, so no page is published just because a keyword looked easy.
KeywordTool.io | Multi-Platform Keywords
KeywordTool.io pulls autocomplete suggestions from Google, YouTube, Amazon and other platforms. I use it for e-commerce clients to understand how buyers describe products on Amazon versus Google, and to find product attributes and questions that belong on category and product pages, not only in the blog. For stores, this is often where the most valuable long-tail terms hide, because shoppers use different language on marketplaces than on Google, and matching that language on product pages lifts both rankings and conversions.
LowFruits | SERP Weakness Finder
LowFruits flags keywords where forums, thin pages or weak domains rank on page one. I use it to prioritise which pages in a topical map to publish first, because those quick wins build the traffic and trust signals the harder pages need. Its clustering also helps me merge overlapping keywords into one page. Every quick win still has to fit the central entity of the site. A low-competition keyword that pulls the site off-topic can dilute topical authority, so I only publish the ones that strengthen the core clusters in the map.
Keyword Planner | Google's Demand Data
Google Keyword Planner shows search volume ranges and commercial competition straight from Google Ads data. I use it to sanity-check volumes from third-party tools, to judge the commercial value of a topic by its bid ranges, and to find local keyword variations for small businesses that serve a specific city or region. Because it shows commercial intent so clearly, it also helps me decide which pages deserve the strongest internal links and the most investment, so each client’s budget goes to the topics that are most likely to produce leads and sales.
8. Content Optimisation
Once the map is set, every page has to cover its entities fully and read well. These tools check both before anything is published.
NeuronWriter | Semantic Optimisation
NeuronWriter analyses the top-ranking pages for a query and suggests the terms, entities and questions a complete answer should cover. I use it as a checklist after the draft is written, never as the writer, to confirm a page covers its topic fully without stuffing terms. Its content score is a guide, not a target, and I always read the competing pages myself before accepting a suggestion. For large sites, it also makes it easy to audit many existing pages quickly and pick the ones worth updating first. Where a suggestion doesn’t fit the reader’s intent, I leave it out, because a natural page beats a perfect score.
Frase | Briefs & SERP Research
Frase speeds up content briefs by pulling headings and questions from the top results for a query. I use it to see how competitors structure a topic, then build my own brief from the topical map, so each page has a clear main intent, the right supporting questions and the internal links it needs to support the cluster. The difference is that the topical map decides what the page covers, not the tool. Frase just helps me see the current SERP quickly, which saves hours of manual research on every brief and keeps writers focused on the right intent from the first draft to the final edit.
Surfer SEO | On-Page Benchmarks
Surfer compares a page with its top-ranking competitors on structure, length, headings and term usage. I use it to benchmark existing client pages during audits and find the ones that fall far below the standard for their query. Those pages are usually the fastest to improve, because the gap is clear and the fix is focused. I pair its suggestions with entity analysis so the page covers meaning, not just matching words. Agencies like it because its reports are easy to share with clients who want a clear number to track progress. For store owners, I use it most on category pages, where thin copy is the usual problem and a short, well-structured section can lift rankings quickly.
Grammarly | Clarity & Correctness
Grammarly is the final pass before any page goes live. I use it to catch grammar and spelling errors and to tighten long sentences, which matters because AI engines prefer to quote clear, direct statements. Short, well-formed sentences are easier for a model to extract and cite accurately, and they are easier for your buyers to read on a phone as well, so the same edit helps both audiences at once. For agencies that send me drafts from their writers, this pass also keeps quality consistent across many clients. I check facts, names and figures by hand afterwards, because no tool can confirm that a claim is true.
9. SEO Automation
Automation turns hours of repetitive audit work into minutes, so more of every client budget
goes into strategy and the fixes that matter.
Python | Audit & Data Pipelines
Python is the backbone of my automation. I write scripts that pull SERP and LLM mention data from APIs, crawl sitemaps, cluster keywords, check internal links and build reports in Excel or Word. A pipeline written once runs for every client, which keeps audits consistent and lets me check many more prompts and pages than I could manually. The same scripts power my AI visibility audits, from pulling prompt data to scoring each result. For agencies, this means a repeatable process that scales across many client sites, with the same quality on the tenth audit as on the first.
n8n | Workflow Automation
n8n connects APIs, spreadsheets and AI models into scheduled workflows without heavy code. I use it for repeat jobs such as weekly rank and AI mention checks, alerts when a key page drops, and moving audit data into client reports, so nothing depends on me remembering to run a script. It is self-hostable, which keeps client data under control and keeps running costs low as the number of workflows grows. Clients see changes as they happen instead of weeks later, in their inbox or dashboard. I also use it to connect client forms and CRMs, so new leads are logged automatically.
Claude Code & MCP | AI-Assisted Builds
I use Claude Code to build and maintain my SEO pipelines, and MCP connectors to give AI direct access to tools like DataForSEO and Search Console. That means I can ask a question about a client’s data in plain language and get an answer backed by live numbers. It is how I built my content pipeline, which follows Koray’s Semantic SEO framework from topical map to brief to draft. Every output is still reviewed by me before it reaches a client, so automation adds speed, never guesswork. I also build quick one-off tools with it, such as a schema checker, when a client needs something no plugin handles.
OpenRouter | Multi-Model Access
OpenRouter gives one API to many AI models. I use it with evaluation tools to compare how different models answer the same SEO task, such as classifying search intent or writing a brief, then choose the most accurate and cost-effective model for each step of a pipeline. Testing models side by side keeps my automation reliable when new models launch or old ones change their behaviour. It also avoids lock-in to one AI provider, which protects clients from sudden price or policy changes. For clients, that means lower running costs and results that stay stable over time, with less rework.
10. WordPress & Shopify
Most of my clients run on WordPress or Shopify, so I optimize both platforms at the theme, template and structured data level.
WordPress | Flexible CMS for SEO
WordPress runs most of the service and content sites I work on. I use it to control URL structure, internal linking, schema and templates in detail, which matters when a topical map has hundreds of pages. I also fix the common plugin and theme issues that slow sites down or hide content from crawlers. I also set up clean category and tag structures, breadcrumb schema and author pages, so every article clearly connects to the site’s central topic and to a real expert. That structure is what turns a set of posts into a topical authority.
Elementor | Page Builder
Elementor is fast for building landing and service pages, but it can add heavy markup. I use it with care: clean heading structure, minimal nested sections and optimised assets, so pages keep their design without hurting Core Web Vitals. I also fix heading levels, which Elementor makes easy to get wrong, because AI engines rely on them to understand structure. When a site is already built on Elementor, I optimise it rather than rebuild it, which saves the client time and money while still delivering real speed gains.
Shopify | E-Commerce SEO
Shopify powers many of the stores I work with. I optimise collection and product templates, fix duplicate URLs created by collection paths, add Product and Review schema, and structure product attributes so they appear clearly in Google Shopping, AI Overviews and AI shopping answers. Store owners get pages that rank and convert without a rebuild. I also build helpful buying guides and comparison content inside the store, linked to the right collections, so the store earns informational traffic that AI engines cite and converts it into sales.
GeneratePress | Lightweight Theme
GeneratePress is my default theme for content-heavy WordPress sites because it loads fast and outputs clean HTML. For clients planning large topical maps, a lightweight theme keeps every new page fast as the site grows, and its hooks let me add schema and internal link modules to templates without extra plugins. A clean base makes technical SEO easier to maintain for years, not just at launch. It also works well with Elementor for key landing pages, so clients get design freedom where it matters. Fewer plugins also means fewer updates to manage and fewer security risks.
11. Outreach & Reporting
Good work needs to be seen. These tools help me reach the right people and report results in a way that every client can understand.
Lemlist | Personalised Cold Outreach
Lemlist powers my personalised outreach to agency owners and businesses. I send short emails built around one real finding from their site, such as a prompt where AI search names a competitor instead of them, with careful follow-ups and deliverability settings. The same approach works for digital PR and link building campaigns I run for clients. Every campaign is small, targeted and written by hand, because generic mass email damages sender reputation and rarely earns replies from busy owners.
Looker Studio | Client Dashboards
Looker Studio turns Search Console, Analytics and my own AI visibility data into dashboards clients can open any time. I build reports that show rankings, AI mentions and traffic together, so agency owners and business owners see progress in one view instead of chasing spreadsheets. White-label versions let agencies share them with their own clients. Each dashboard is built around the client’s goals, such as leads, sales or calls, so reports focus on business results rather than vanity metrics.
HubSpot Sales | Advanced Marketing CRM
HubSpot keeps every lead, conversation and project stage in one place. I use its free CRM to track outreach replies, audit requests and client projects, so no conversation is lost and every follow-up happens on time. For agencies that partner with me on white-label work, it also keeps shared projects and deadlines clear for both sides. Clients always know where their project stands, what has been done and what comes next, without having to ask me for an update or search through old emails.
Canva | Audit Visuals & Reports
Canva is where I turn audit findings into visuals that decision-makers actually read: before-and-after screenshots, prompt tables, topical map summaries and one-page action plans. A clear visual gets approval for fixes faster than a long spreadsheet, and white-label templates let partner agencies present my work under their own brand. For small businesses, I also design simple one-page summaries that owners can share with their teams to agree on next steps quickly. Every visual is built from real audit data, never mock numbers or invented results.
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