SEO AI Tools

Greta.sh is an AI app builder and vibe coding platform that turns a described idea into a live, production-ready full-stack website or web app. You type what you want to make, Greta.sh builds it on screen, and it can be deployed with one click. Alongside the builder, Greta.sh offers Self-Growing Websites: sites that find what customers search for, write pages that answer those searches, and get you showing up on Google, ChatGPT, Perplexity and everywhere else. The pages it produces are content you review and approve. The platform is positioned as one place to design, build, launch, manage and grow a product, from first idea to live product. The starting point for the product is a question every builder recognises: you built a website or an app, so why is nobody visiting? Getting found on Google and on AI tools such as ChatGPT and Perplexity takes months of SEO and AEO work. Greta.sh frames its Self-Growing Websites offering as the fix for that gap. Instead of treating search visibility as a project you complete once, the site is designed to keep growing itself: Greta.sh finds what customers search for, writes pages that answer them, and works to get the site showing up on Google, ChatGPT and elsewhere. The Product Hunt listing summarises the pitch simply: don't just build a website anymore, build a customer magnet with Greta.sh. At the centre of the platform is a four-step build flow. Step one is to describe your idea: you type what you want to make, like you are talking to a friend, and the content stresses that no special skills are needed — your idea is enough. Step two is to see it built, as the idea starts turning into something visible on screen. Step three is to bring your own tools, connecting Claude, Gemini, Slack, GitHub and more so that Greta.sh brings it all together. Step four is to deploy with one click, where everything from your domain to search ranking to safety checks just works, with the promise that you can build today and launch today. The Full Stack feature supports this: you can build your full app, website and tools without writing a single line of code, and the platform distinguishes between a Self-Growing Website, a Web App and full stack, with a "Which one do I need?" prompt to help you choose. The Self-Growing Websites capability is the growth side of the platform. Rather than leaving search visibility to manual keyword research and page writing, Greta.sh finds what customers search for, writes pages that answer those queries, and keeps adding pages that you approve. Because the content is subject to your review and approval, you stay in control of what goes live while the system handles discovery and drafting. The stated aim is presence in both traditional Google search and AI search tools, which is where the SEO and AEO combination comes in: the pages are built to answer the questions customers actually ask, so the site can appear in search results and in the answers AI assistants give. The Greta.sh MCP is the control layer for people who prefer to work inside their existing AI assistant. With the MCP, you can run the platform from Claude, ChatGPT or Cursor: you ask it to build, grow, publish or report, and the work is carried out through the connected assistant. The MCP feature is described as connecting your tools, systems and data so everything works together smoothly. In practice this means the build-and-grow workflow does not require switching into a separate dashboard each time — a builder can stay in the assistant they already use and issue instructions in natural language, whether that is to add pages, publish approved content or produce a report. Beyond building and growing, Greta.sh bundles the supporting pieces a live site needs. Domain lets you launch your brand online with your own custom domain in minutes, and the deploy step folds domain setup together with search ranking and safety checks. Asset Manager keeps files, images, videos and content in one clean, organised place, so the media that supports your pages is managed rather than scattered. Templates give you a head start: the platform advertises a choice of 100+ templates, where you find one you like, click it, and Greta.sh does the rest — the library includes published examples such as an e-commerce frontend, an analytics template, a content dashboard, a marketing template and agency or portfolio-style sites. Support scales with your plan: community support on Free, email support from Starter, and priority support on Pro and Business. The overall approach is to keep the whole lifecycle in one platform rather than stitching together a builder, a host, a domain registrar and a content team. You begin with a prompt, watch the build appear, attach the tools you already use, and deploy — then the same platform keeps working on growth by finding search demand, drafting answering pages and waiting for your approval before publishing. The MCP extends the same control surface into Claude, ChatGPT and Cursor, so the build, grow, publish and report actions are available wherever you already work. The stated benefits follow from that loop. You can go from idea to live product quickly, because deployment handles domain, search ranking and safety checks and the platform promises you can build today and launch today. You do not need to write code, since the full-stack capability covers apps, websites and tools. You do not need to run a separate SEO and AEO programme from scratch, because the site is designed to find what customers search for and write pages that answer them on your approval. And you get a single place to design, build, launch, manage and grow, rather than a set of disconnected tools. Concrete scenarios described in the content include building a full-stack web app or a website from a plain-language description; launching a brand on a custom domain; and running a site that keeps adding approved pages aimed at what customers search for. The MCP scenario is explicit: running the platform from Claude, ChatGPT or Cursor and simply asking it to build, grow, publish or report. The template library suggests additional starting points, with published examples including an e-commerce frontend, an analytics template, a content dashboard, a marketing template, and agency and portfolio-style sites. The platform is presented for people with an idea rather than a development team — the build flow explicitly says no special skills are needed, just your idea. The connector wall shows the tools the product works with: GitHub, OpenAI, Slack, AWS SES, Resend and Netlify, plus Claude, Gemini, MongoDB, Google Analytics, Stripe and xAI. Pricing detail in the content is limited to support tiers: a Free plan with community support, a Starter plan with email support, and Pro and Business plans with priority support. The Product Hunt listing categorises the product under Marketing and SEO. Greta.sh combines an AI app builder with a growth engine: describe your idea, get a production-ready full-stack site or app, deploy it with one click, and let the platform find what customers search for and draft the pages that answer them for your approval. It is aimed at getting a new site found on Google and in AI tools like ChatGPT and Perplexity without months of manual SEO and AEO work, and at keeping build, growth and publishing under one roof.

Dedica is an agent readiness testing tool for websites. It tests your site the way AI agents use it and then gives your team a fix for every place those agents fail. The product is aimed at teams whose websites are visited, compared and signed up on by AI agents such as Muse, Dots, Instinct, ChatGPT, Claude, OpenClaw and GrokBot. A free score is delivered in under a minute with no signup required, and the detailed fix kit is purchased separately. Dedica's stated purpose is simple: find where agents get stuck on your site, and hand your team the fix. Most websites are built for people and fail for software. Dedica's site names the specific failure modes: bot checks, pages that only render in a browser, and APIs with no public docs. When an agent hits one of these, it does not report the problem — it simply moves on, which means the business never learns it was skipped. Dedica cites two figures to frame the stakes: only 6% of business websites are ready for AI agents, and businesses that are ready are recommended by AI agents 1.9× more often. The core problem Dedica addresses is that this failure is invisible by default; there is no error report when an agent quietly abandons a sign-up flow, so teams have to test for it deliberately. The free report runs 125 checks across four areas: discovery, access, usability and payments. Discovery asks whether agents can find what they need on your site at all. In the sample report this area is scored out of 20 and includes checks such as whether an OpenAPI or Swagger spec is published at the usual paths, and whether an MCP server or manifest can be found. The fix kit's file list shows further discovery-oriented items, including a check for an llms.txt file and an agent-friendly 404 page. Each discovery gap is a place where an agent has nothing machine-readable to work with, so the practical result of fixing them is that agents stop guessing and start reading structured information about your product, plans and endpoints. Access checks ask whether agents are let in once they find the right page. The sample report scores access out of 30, and the top-ranked issue in that sample is a bot check that blocks agents at sign-up: acme.com/signup returns a 403 challenge page. Dedica shows what the agent actually received — a challenge page with a reference ID — next to a comparable page that returns plans, prices and a working sign-up without a bot check. The fix instruction is explicit: let verified AI agents through the bot check on public pages and sign-up while keeping rate limits in place. OAuth support appears as another item in the fix kit, covering how agents authenticate rather than being turned away. Usability checks ask whether an agent can read and act on what it finds. This is the largest check group in the sample report, scored out of 40. One issue shown is pricing that needs JavaScript to load: acme.com/pricing is empty until scripts run, so an agent that fetches the page sees no plans or prices even though a human in a browser sees a full pricing table. Dedica contrasts this with a page it can use, where plans and prices sit in the page text and the sign-up action works. The corresponding fix file, 02-content-no-js.md, addresses rendering content without JavaScript. Usability work matters because an agent that cannot read your pricing cannot compare it, and an agent that cannot complete a sign-up cannot convert on your behalf. Payments is the fourth check area, scored out of 10 in the sample report. It covers whether an agent can get all the way through a commercial action — signing up for a plan, for instance — rather than being blocked at the last step. The sample report shows an overall score of 71 out of 100 with a grade of D, and 42 fixable issues spread across discovery (11/20), access (9/30), usability (14/40) and payments (4/10). Presenting the four sub-scores separately tells a team which layer of the problem to attack first rather than treating agent readiness as a single pass or fail. Every finding is ranked by how much fixing it would move the score. The report's Fix first list puts the highest-impact issues at the top with an estimated gain: a bot check blocking agents at sign-up (+6.2), pricing that needs JavaScript (+4.6), no OpenAPI spec published (+4.2), and no MCP server (+4.1). Each issue carries check points, such as 0/6, so a team can see how much of a given check is currently passing. The fix kit then turns each finding into one instruction file per issue, written for a developer or a coding agent, stating what to change and how to check it worked. In the sample, the kit contains 42 files, named so they map directly to the issue — 01-bot-detection.md, 02-content-no-js.md, 03-openapi-spec.md, 04-mcp-server.md, 05-llms-txt-exists.md, 06-oauth-support.md, 07-agent-friendly-404.md and 35 more. The workflow is scan, fix, scan again. First, you enter your domain; the score is free and takes under a minute. Second, you hand over the fix kit — either giving the files to your developer or pasting them into Claude Code, Codex, Cursor or GitHub Copilot. Third, once the change ships, you rescan and watch each check pass. The sample walks through exactly this loop, with a score of 38 turning into 71 after the fixes are applied. Because the fix files are written for coding agents as well as people, the same artifact can be routed into an existing agent-assisted development workflow without rewriting the instructions. The outcome Dedica describes is a site that agents can actually use: they find the pricing page, read the plans and open the sign-up flow, instead of hitting a 403 challenge page and silently giving up. Teams get a documented, per-issue record of what was wrong and how to verify the repair, which removes the guesswork from a class of problem that produces no error log. Dedica also frames the upside commercially — businesses ready for agents are recommended by AI agents 1.9× more often — so the work is positioned as revenue-relevant rather than purely technical hygiene. Concrete scenarios in the content include a team asking an agent to sign up for a plan and discovering the site blocks it; a pricing page that renders in a browser but arrives empty to an agent; and an API with no published spec or MCP server that leaves agents with nothing to call. The product suits teams shipping websites with pricing and sign-up flows, developers maintaining public APIs, and teams already using Claude Code, Codex, Cursor or GitHub Copilot who want remediation delivered as instructions those tools can execute. Pricing is explicit: the score with 125 checks is free, no signup is required, and the fix kit is $30 one-time per site. Dedica also lists Claude Code, Codex, Cursor and GitHub Copilot as ready targets for its fix files. In short, Dedica makes agent readiness measurable and fixable: it scores how agents find, read and use your website, ranks the issues that cost you the most, and hands your team an instruction file for each one so a developer or a coding agent can close the gap and verify the result on the next scan.

ogimagegenerator.dev turns any URL into a social preview. Paste a link and it reads the page copy, brand colours, fonts, logo and a screenshot, then designs three on-brand Open Graph images in seconds.The AI never draws pixels. It writes a design spec that deterministic templates render, so text is always crisp and every result can be edited by hand. Formats cover Open Graph 1200x630, X header 1500x500 and square 1080x1080.Export a PNG or copy a hosted og:image URL with ready-made HTML and Next.js snippets. Free, no account.

Bottally is an AI marketing agent for SEO and AEO. Connect your site once and it researches your keywords, audits your pages and finds the topics your competitors cover and you don't. Then it writes and publishes one researched article to your blog every day (WordPress, Webflow, Ghost, Shopify, Wix, Notion, Framer, Next.js or any webhook). It tracks whether ChatGPT, Perplexity, Google AI Mode and AI Overviews mention your brand, and matches you with real businesses for do-follow backlinks.

Salience is server log intelligence for search engines and AI crawlers. It turns the server and CDN data a website already generates into a live view of every request, response and issue coming from people, search engines and AI bots. A user connects a source they already run — Cloudflare, Vercel, CloudFront, Netlify, Kinsta, Shopify, Apache, Nginx or raw log files — and Salience starts reading, verifying and classifying requests within seconds. The dashboard shows which pages Googlebot, GPTBot, ClaudeBot and other recognised crawler identities requested, the exact response the infrastructure served, and where crawl budget was wasted. Salience is built for search and organic growth teams, AI and content teams, security and platform teams, and engineering and commerce operations. The problem Salience addresses is a visibility gap between what analytics reports and what actually reaches a site. Google Analytics measures users, sessions and events collected by its tag and filters known bots out by design, so crawler behaviour never appears in it. Search Console answers some questions but not in real time: as one quoted scenario puts it, sitemap changes deployed yesterday may go unseen for 48 hours. Meanwhile raw access logs contain the answers but are difficult to read. Four situations recur in the content: not knowing whether Googlebot has seen sitemap changes, not knowing which AI systems read your content or whether they are genuine, discovering that traffic claiming to be Googlebot is not and that the real crawler is being rate-limited by a WAF, and shipping a URL change only to have Googlebot hit thousands of 404s before anyone notices. Salience exists to make those situations visible immediately. Two of the four capabilities in the Salience engine are Observe and Verify. Observe means seeing what is actually reaching the server: Salience analyses requests arriving through a CDN or web server, using server-side data only, with no browser script and no sampling by Salience. Every request that reaches the source is ingested, humans and bots alike, and each is classified as human or bot with more than 200 crawler identities recognised. Verify addresses impersonation: supported search, AI and automated identities are checked against official provider IP ranges wherever verification data is available, so legitimate GPTBot, ClaudeBot or PerplexityBot traffic can be separated from scrapers that merely reuse the User-Agent string. Verification status, per-bot crawl frequency and top URLs live on the Bots and Crawlers page. The other two capabilities are Explain and Act. Explain connects each request to the path, status code, site section, historical pattern, sitemap, robots.txt rule or index state, so a change in behaviour has a cause rather than just a number. Act moves from evidence to action: the affected bots, IPs and URLs are shown alongside the likely issue and a recommended next step. This surfaces through alerting that names the crawler, the URLs and the next step instead of saying that something happened. Example alerts in the content include 15,000 404s on /products/* from Googlebot since a 14:00 deploy, with a suggested next step to roll back or fix the URL structure, and ClaudeBot refused with 403 on 32 URLs under /collections since a WAF rule change, with a suggested next step to review the rule. Alerts can be marked reviewed, snoozed or dismissed as expected, and every alert checks that the site is reachable first. AI crawler activity gets its own view. Requests are grouped into training and other crawlers, AI search crawler requests and user-triggered AI fetches, with totals, share of traffic, unique crawlers and pages crawled. The AI Crawlers panel lists each bot with its category, verification status and unique page counts, and can be exported to CSV. Salience states clearly that recorded requests are classified by agent and do not prove training, indexing, answers or citations. For day-to-day work the dashboard gives a live view per site with shared read-only links for clients and colleagues, alerts arrive by email, Slack and webhooks, and reports provide a weekly summary per site plus CSV exports of the record behind it. A REST API with a personal API key is available on every plan including Free, a CLI lets teams query the same record from a terminal or script, and an MCP server connects Claude, Cursor or ChatGPT to the crawler data so users can ask questions of it. The overall method is a pipeline from raw request to recommended action. Signals feed the engine from search crawlers such as Googlebot and Bingbot, AI and LLM bots such as GPTBot, ClaudeBot and PerplexityBot, platform signals from Google Search Console and Google Analytics, and the primary source, CDN and web server logs. Requests move through raw, normalised, enriched and understood stages before producing Observe, Verify, Explain and Act. Setup follows three steps: connect what you already use, after which Salience starts reading requests and makes them available in the dashboard within seconds, and then act on problems that become obvious. Nothing is added to the page, there is no frontend change, and CDN sources need no agent on the origin. The stated benefits are complete visibility of real requests from people, search engines and AI agents using existing infrastructure; finding errors, blocked crawlers, missing pages and suspicious traffic early without manually digging through logs; understanding which content gets crawled, how the site responds and where problems need attention; and setup in minutes. Salience also runs 16 detectors checked every 5 minutes. On data protection, Salience states that it processes data only to provide the service, with the customer remaining the controller; core data is hosted in AWS London (eu-west-2) and encrypted at rest and in transit; collection is server-side with no client-side pixel; and logs are not sold, used for advertising or shared between customers, with a DPA available on request. Concrete workflows described in the content map to specific teams. Search and organic growth teams use it to check whether Googlebot has seen sitemap changes without waiting for Search Console. AI and content teams use it to see which AI systems read their content, whether the traffic is training or retrieval, and whether it is genuine. Security and platform teams use it to separate spoofed Googlebot traffic from the real crawler and to catch the real one being rate-limited by a WAF. Engineering and commerce operations use it to spot a URL change that produced thousands of 404s. It is also positioned for e-commerce intelligence and publisher intelligence. Historical imports support retrospective investigation: Apache, Nginx or CloudFront logs, or an export from another log analyser, can be dragged in, are auto-detected, deduplicated against real-time data and merged into the dashboard without counting against the monthly request quota. Setup is guided in about ten minutes or less for Cloudflare, Vercel and Kinsta; Apache and Nginx need someone with root or sudo for roughly ten minutes, using a read-only agent that reads the log file and sends it over HTTPS without sitting in the request path. Where a Worker collects logs, on Cloudflare and Shopify, it adds a small amount of edge compute. CloudFront real-time logs arrive at the sampling rate the customer configures. Plans scale with traffic: Free at $0 covers up to 12K visitors a month, one website, 30 days of history and two email alerts; Solo at $19/mo covers up to 125K visitors with six months of history and all 16 alerts by email and Slack plus sitemap and Search Console; Starter at $49/mo covers up to 500K visitors, five websites, one year of history, webhooks and shared dashboard links; Growth at $149/mo covers up to 2.5M visitors, 15 websites and two years of history with priority support; Pro at $499/mo covers up to 12M visitors, 50 websites, four years of history and dedicated support with a shared Slack channel; Enterprise is custom with unlimited websites, SSO/SAML, audit log, uptime SLA, DPA and custom MSA. Salience does not replace a WAF, CDN or observability tool; it reads the access logs those systems already produce and answers a narrower question well: what did search engines and AI crawlers request, what did the infrastructure return, and were they genuine. The core value is evidence from your own logs, turned into a live view and a clear next step.

MentionBird is an AI visibility tool that shows brands why AI assistants recommend their competitors instead of them — and what to do about it. When buyers ask ChatGPT, Perplexity, or Gemini what to buy, the answer is assembled from a small set of sources that the model trusts for that specific question. MentionBird finds those sources for the prompts that drive a company's sales and gives a concrete plan to get the brand into them. It is built for any business whose customers research products or services online, and it positions itself as the "Ahrefs for AI Search." Buyers no longer start with ten blue links. They ask an AI assistant, get two or three brand names, and shop from that shortlist. The shortlist now forms inside the answer itself, and if AI does not say your name, you were never in the running — and you would never know. AI answers are built from a small set of pages, reviews, and videos that the model trusts for that specific question. Get into those sources and you get recommended; stay out, and your competitor takes the customer. Because most categories have no clear winner in AI answers yet, starting early can help a brand become the default answer. The first step is picking the prompts that bring customers. Users add the real questions buyers ask — examples on the site include "Best running shoes for daily use," "Best marathon shoes for men," "Best running shoes for flat feet," and "Most comfortable shoes for long runs." MentionBird then tracks exactly which sources AI cites for each one. For a given prompt, it lists the sites the models check and marks each as cited or not cited — showing, for example, that runnersworld.com and runrepeat.com are cited while a brand's own listings are not. That visibility into the underlying citations is the difference between knowing you are losing and knowing where to go to fix it. For any tracked prompt, MentionBird generates AI Ranking Advice. It reviews the answers, the named brands, and the cited sources, then recommends practical steps and shows the effort required for each one. Advice can include getting featured in trusted roundups and comparison guides, publishing guides that answer the questions buyers ask before choosing, adding clearer product facts such as fit and durability details to product pages, or earning videos and mentions from trusted websites. Each recommendation is labelled with impact and effort — for example, "Medium effort · High impact" — so teams know what to fix first. The platform checks and updates AI visibility data once every 24 hours. It tracks ChatGPT, Perplexity, Gemini, and Claude, as well as Google AI Overviews, Grok, Microsoft Copilot, and DeepSeek. Tracking is prompt by prompt: cited, not cited, or no brand named yet. Users see share of AI recommendations versus each competitor week over week, and every score change is traced to the exact prompt, model, or citation that caused it. A weekly brand visibility share view can show, for instance, one brand up four points while another drops three. MentionBird describes itself as built around the question that matters: what do I do to get mentioned for this prompt? It contrasts itself with other AI visibility trackers that show a mentions chart, tell you when you dropped, and leave the "now what?" to you. MentionBird instead shows the sources behind every answer, gives a per-prompt plan to win them, and proves the plan worked with daily tracking. It also distinguishes itself from SEO rank tracking: SEO tools show a webpage's position in Google search results, while MentionBird checks whether AI answers mention your brand, which sources AI cites, and how your visibility compares with competitors. The outcome MentionBird promises is movement from "never mentioned" to the brand AI often mentions. Users can watch themselves become the answer, because every plan they execute shows up in the numbers. Teams quoted on the site describe concrete gains: Risely discovered it won the category questions but lost the comparison ones, which it called exactly backwards for a buying decision; Facets learned the exact questions where engineering buyers saw it as invisible; Recooty found AI was recommending an ATS to every buyer who asked, just never theirs; Superfans reported being in 83% of "alternatives to the category leader" answers after MentionBird found the gap; and Helixo, despite 3300+ reviews, could pinpoint the questions where AI still was not recommending it. Typical use cases start with evidence gathering: a brand runs the free AI Visibility Report to see whether AI recommends its competitors, then adds its brand, prompts, and competitors to track the questions that matter. Marketing and SEO teams use prompt-level data to decide which pages, guides, or product details to fix first. Agencies manage several clients in one workspace, using tags to organize prompts by brand, product, or project. E-commerce and software companies watch comparison and category prompts separately, since the two can behave very differently. Multi-brand teams keep several brands or product lines in a single workspace, and anyone entering a new category can move early to become the default AI answer before a clear winner emerges. MentionBird works for any business whose customers research online, including e-commerce stores, software companies, healthcare providers, law firms, financial services, real estate, travel, and education, plus home services, automotive companies, and local businesses. Agencies, marketing teams, and SEO teams are explicitly supported, and the site suggests starting with 20 to 50 real buying questions rather than hundreds of broad prompts. No installation is required — there is no code, plugin, or tracking tag to add to a website, and setup takes only a few minutes. Pricing includes a free AI Visibility Report tool and a Starter plan at $19/mo for three months with the Product Hunt code PH19. Plans can be changed anytime, with upgrades applying immediately and downgrades beginning with the next billing period, while saved prompts, labels, and past data remain available. MentionBird's core value proposition is simple: it turns AI visibility from a chart you cannot act on into a per-prompt plan you can execute and verify. By revealing the sources behind every AI answer, explaining why competitors get recommended, and tracking progress daily across the major AI platforms, it helps brands get into the shortlist that now forms inside the answer — before their competitors do.

Manifold is a hosted MCP server that gives your AI agent live marketing data across every channel you grow on. Rather than opening another dashboard, you connect Manifold to the AI app you already use — Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini CLI, Grok, OpenCode or any other client that supports remote MCP servers — and ask questions in plain words. Your agent picks the right tools and brings back answers built on real data: Google rankings and backlinks, what AI assistants say about a brand, social posts and videos, Reddit threads, competitors' live ads, and companies and people with verified work emails. It is aimed at marketers, founders and growth teams, and it is free to start for every new workspace. Marketing data is usually spread thin. Rankings live in one tool, AI answer visibility in another, creator research somewhere else, competitor ads in an ad library, and buyer intent across LinkedIn, X and Reddit. Pulling that together means a pile of tabs, several logins and API keys for services like Reddit, LinkedIn or a search data provider. Manifold collapses it into one hosted connection: your agent signs in once and uses Manifold's own provider accounts, so there is no Reddit API app, no LinkedIn login and no DataForSEO deposit to manage. The result is a single place where an agent can look up live marketing data whenever it needs to, and answer with real numbers rather than guesses. SEO is the first data group. Your agent can see every keyword you rank for, who outranks you and why, and what to fix on your site. A typical question — which keywords do we rank 5 to 15 for that could reach page one? — returns terms sitting just below the first page. A sample answer might tell you to put a phrase in a page title because you already rank seventh for it, to answer a question directly on the page because an AI assistant gets it wrong, or to link to that page from your best-ranking posts. Because the data is fetched live from search providers, the recommendations reflect current rankings rather than a stale export. AI answers, sometimes called AEO, covers how assistants describe your brand. Your agent can ask ChatGPT, Claude, Gemini, Perplexity and Google's AI Overviews any prompt, on demand, and report which brands each answer names, in what position, and which pages it cites. You can check whether AI assistants recommend you for the questions your buyers ask, see who they name instead, and review which pages they cite. You pay per prompt and engine rather than a monthly fee for tracked prompts, and a free check shows whether your site lets AI crawlers read it — a prerequisite for appearing in AI answers at all. Social research finds the creators and videos already winning your niche. Your agent can locate the TikTok, YouTube and Instagram creators your buyers watch, along with their audience and their top videos with transcripts — for example, which sales creators on TikTok and YouTube get the most views and what their top videos are. Instagram, Facebook, LinkedIn and X are covered too. Ad libraries sit alongside this: you can pull competitors' live ads on Meta and Google, see what each one says, and check how long it has run, so you can write better ads by seeing what already works. Reddit and lead research connect that data to action. Your agent can find every thread that asks for what you make, read the replies and draft your answer — for instance, threads this month asking for a CRM, or Reddit threads asking for recommendations. On the people side, Manifold spots posts on LinkedIn and X about the problem you solve and returns each author's verified work email, so you can reach buyers who are looking right now. The agent writes from this data too: blog posts, Reddit replies, ad copy and outreach emails are drafted in the chat with real numbers behind them, and you check the drafts and use them where you want. Manifold is the data your agent works from — not a scheduler or an autoposter. Under the hood, Manifold is a remote MCP server. You connect your agent once — in Claude, add the URL as a custom connector and sign in; in Claude Code, run claude mcp add --transport http manifold followed by the URL — and every client uses one URL, https://mcp.manifoldmcp.com/mcp, and one credit balance. Tools carry names such as seo_get_ranked_keywords, aeo_run_ai_answers, reddit_search_posts and linkedin_search_posts, and your agent picks the ones it needs for your question. Manifold fetches data live from specialist providers when your agent asks: search, keyword, backlink and AI answer data come from DataForSEO, while Reddit, TikTok, Instagram, YouTube, Facebook, LinkedIn, X and the ad libraries come from the platforms' public data. Results are cached briefly — most for a day to a week — so a repeat question comes back fast and costs less. Answers you can act on are the point. Instead of raw exports, the agent returns specifics: what to fix on a page, who to reach out to, which threads to join. From there it can take the next steps for you, drafting the content or the outreach in the same conversation. You get real numbers rather than estimates, one balance and one URL across every client instead of a collection of keys and logins, and cost control built in — every call is paid from a single credit balance, each tool states its price before it runs, and your agent can cap what a call spends. Concrete workflows run across the product. In SEO, teams ask which keywords they rank 5 to 15 for that could reach page one. In AI visibility, they ask whether they are recommended for a query like "best CRM for startups". In social, they ask which sales creators on TikTok and YouTube get the most views and what their top videos are. In prospecting, they ask who posted about leaving their CRM this week. In competitive research, they ask what a competitor is running on Meta this week. And in community marketing, they ask for threads posted this month asking for a CRM. Manifold is for marketers, founders and growth teams doing SEO, AEO, social, ads or lead research, and it works with Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini CLI, Grok, OpenCode and any other client that supports remote MCP servers. Clients without OAuth use a Manifold API key instead. Pricing is easy to start: every new workspace gets 250 free credits, then a Pro plan from $20 a month for 2,500 credits, or Max at a lower rate per credit, with top-ups from $10 between renewals. Manifold is a marketing data MCP server founded in London in September 2026 by Jesse Sibley, and it is not related to Manifold Markets. Manifold MCP turns the AI assistant you already use into a marketing research and growth partner. One connection, one credit balance, and live data from Google, AI answers, Reddit, social platforms, ad libraries and lead sources — so your agent can answer in plain language, then help act on what it finds.

ReSO is a generative engine optimization platform that helps brands show up and get recommended in AI search results. It is built for founders, CMOs, and growth teams who want to turn buyer intent expressed in ChatGPT, Perplexity, and Google AIO into visibility, citations, and traffic. The product is pitched as an agentic employee you onboard so that your brand is recommended in the answer when buyers ask AI. Instead of only tracking rankings, ReSO analyzes intent, competitors, content gaps, and technical issues, then turns them into priority actions to improve AI recommendations. The site also invites visitors to check their AI visibility and to run a detailed AI visibility analysis across brand, content, technical setup, and competitive gaps worth $500 at no cost. The problem ReSO addresses is a shift in how buyers discover products. As the site puts it, your buyers are searching on ChatGPT, and in 2026 AI will dictate discovery. B2B buyers increasingly use ChatGPT, Perplexity, or Google AIO to research solutions, compare vendors, and shortlist providers. If a brand is absent from those answers, competitors can influence the buying journey before a prospect ever reaches the website. The FAQ frames the discipline as AI Search Optimization (AISO) and notes that AISO, GEO, AEO, and LLMO are overlapping terms for optimizing visibility in AI-generated answers: AISO focuses broadly on AI search, GEO on generative engines, AEO on direct-answer visibility, and LLMO on large language models. The same FAQ observes that most organizations still cannot clearly say how ready they are for AI search, which makes it difficult to identify gaps, prioritize investment, or measure progress. The core of the platform is a proprietary intent model mapped to real buyer queries on AI. ReSO says LLMs interpret intent, not keywords, and its proprietary model analyzes 30+ intent-driven search variables to decode exactly what customers are looking for. Users can curate a list of 75 high search intent-based prompts, view brand share of voice across the entire awareness-to-conversion funnel, and map visibility against competitors. The features section illustrates the Discovery stage of the buyer journey, described as exploration where people look for the best options, trends, and possibilities before narrowing down what fits their needs, with example prompts such as “Best budgeting apps for managing personal finances?”, “Best fitness trackers for everyday use?”, and “Best tools for automating B2B lead qualification?” Citation Intelligence helps create content that gets a brand mentioned by LLMs. Each piece is built around the signals that influence AI visibility: prompt trends, citation behavior, and content gaps across the category. The approach promises a consistent weekly publishing cadence, a 100% customised content strategy to become a category leader on LLMs, and content created in formats that improve mention potential. The site pairs this with an AI citation chat interface. The underlying logic described in the FAQ is that to be cited by AI search engines, a site needs clear, authoritative answers, strong entity signals, useful source material, and structured content that AI systems can interpret easily. ReSO analyzes which sources AI platforms cite within a category and identifies gaps in content and authority signals. The Technical Engine audits the technical issues that affect how AI systems and search engines access, interpret, and trust a website. It delivers technical edits intended to increase LLM visibility, entity mapping and code-level changes to make the website AI-readable, and improvements to discoverability across search platforms. Alongside these engines, ReSO presents a visibility dashboard: the imagery on the site shows a laptop user dashboard, a competitor gap view, a brand visibility score, and a “new opportunity found” alert. The platform is described as multi-platform search optimization, focused on how a brand shows up across AI search rather than a single engine. The workflow ReSO describes starts with onboarding the agentic employee and checking your AI visibility. From there, you identify the questions your buyers are asking LLMs and where your brand currently appears in those answers. ReSO maps high-intent prompts across the buyer journey, measures your share of voice against competitors, and identifies the content and technical gaps that may be limiting visibility. Those findings are converted into prioritized actions: the pages, topics, and site improvements most likely to increase brand mentions and recommendations. On the content side, pieces ship on a weekly cadence and are shaped by prompt trends and citation behavior; on the technical side, entity mapping and code-level changes are applied so the site is easier for AI systems to read and trust; and throughout, visibility against competitor mentions is tracked. ReSO reports multi-platform search optimization outcomes of 3X traffic increase from LLM-driven search, +38% citation growth month-on-month, +25% signup conversion directly from ChatGPT referrals, and +30% visibility score quarter-over-quarter growth. Three case studies are listed on the site. SMC Global, a publicly listed fintech in India, saw a 132% increase in clicks from ChatGPT, a 162% increase in website clicks (up from 37,200), and a 516% increase in impressions (up from 923,000). An EoR platform in HRTech achieved a 12X increase in citations from LLMs in three months, a 364% increase in blog traffic (from 824 to 3,825 monthly clicks), a 5x increase in AI mentions (up from 10 to 50), and a 41% lift in AI visibility (up from 8%). A US-based LegalTech company recorded a 92% increase in brand mentions (up from 288), 60% total traffic growth (up from 23,400), and 55% impressions growth (up from 2,080,000). The use cases described on the site center on buyer research and comparison. Buyers ask AI for the best budgeting apps for personal finances, the best fitness trackers for everyday use, or the best tools for automating B2B lead qualification, and ReSO aims to make a brand part of those answers. B2B teams use it to appear when prospects research solutions, compare vendors, and shortlist providers. Marketing teams use the weekly content engine to publish material shaped by prompt trends and citation behavior, and the technical engine to remove barriers to being accessed, interpreted, and trusted. Growth teams track whether their brand is mentioned or recommended versus competitors at each buyer-intent stage, and prioritize the biggest visibility gaps. ReSO is aimed at founders, CMOs, and growth teams, and says it is trusted by founders and CMOs across high-growth startups and enterprise; logos shown include SMC Global, Mountmiller, Dhisana, Boundless, Early, 5 Day, Gaia Dynamics, Sprouts, and Strong. The platform positions itself as a replacement for agencies, consultants, and fragmented internal workflows — “replace your SEO agency with an agentic employee” — while working alongside traditional SEO rather than replacing it. Getting started requires no credit card and no commitment, and the site offers a detailed AI visibility analysis across brand, content, technical setup, and competitive gaps worth $500 at no cost for now. Beyond the free visibility check, ReSO directs interested teams to book a call to hire an AI employee that grows their business. In short, ReSO's value proposition is turning AI search from guesswork into a measurable, actionable channel. It combines intent analysis across intent-driven search variables, a curated set of high-intent prompts, a weekly content engine, and a technical audit into one agentic employee, so brands can move from being overlooked in AI answers to being mentioned, cited, and recommended. For founders, CMOs, and growth teams who want to know how LLMs see their brand and what to do about it, ReSO offers both the diagnostic — a free AI visibility check — and the ongoing execution to improve it.

Proofsource is an AI intelligence platform that shows whether ChatGPT, Perplexity, Claude and Google AI Overviews name your brand, who they name instead, which sources they cite, and what to fix. Its stated purpose is to make the AI shortlist visible day by day and engine by engine, and then to close the gaps behind every lost answer. The site names four audiences it is built for: growth and brand leaders, writers and search teams, agencies running many brands, and founders and small businesses. Rather than stopping at a visibility score, Proofsource continues through to drafted fixes drawn from your own content, so a gap in an AI answer becomes a change you approve and publish. Buyers now ask AI for a shortlist. An answer engine names three to five brands and moves on, and if your brand is not one of them there is no second page to rank on and no click to measure. Proofsource's own published measurement illustrates how contested that shortlist is: across 947 citations and four engines, 43.5% of the sources AI cited for category questions were comparison pages — listicles, alternatives and versus pages. In the same scan, 387 different websites were cited across just 80 answers, meaning your own site is one voice among hundreds. A separate branded-question read, done by hand, found that only one of four engines described a brand-new company correctly, while the other three answered from what they already believed. The company also published research stating that 0.8% of AI citations point to the brand's own website. These findings explain why Proofsource argues that rank trackers, which measure blue links, do not answer the question of whether an answer engine recommends you. The core visibility layer answers the first question: are you in the answer? For each brand, Proofsource reports mention rate, share of voice and average position per engine, so you can see the share of buyer answers that name you, how large your share of the named brands is, and where you typically appear. Every number carries a 95% confidence range — the site specifies a 95% Wilson interval — so a bad day never looks like a trend. The platform keeps the full text and a screenshot of every answer, which means a visibility claim can be traced back to the actual engine response rather than a summary score. On paid plans the questions are asked every day, because AI answers shift from day to day and a weekly snapshot can miss the day a competitor enters your shortlist. The free trial runs two scans, today and tomorrow, covering 25 questions on every engine for 200 answers in total. Once visibility is measured, Proofsource surfaces who AI names instead of you. Every brand the engines name for your questions lands on a single leaderboard, ranked by answers and broken out per engine, including brands you never thought of as competitors. Any of them can be added to tracking with one click, so the competitive set grows as the engines reveal it, and the leaderboard shows the specific questions where each competitor beats you. The citation layer then explains why: answers are built from sources, so Proofsource lists every page the engines cite for your questions, which cited pages mention you, and which are open to a pitch, a listing or a correction. It flags cited domains and exact pages, listicles that leave you out, and the pages on your own site that engines read versus the ones they skip. In the Tesla example shown on the site, youtube.com was cited in 29 answers, en.wikipedia.org in 24, reddit.com in 20 and tesla.com in 17 — the first naming Tesla outright, the next two naming it partly, and the last being the brand's own site. Proofsource also checks what the engines get wrong about you. Because engines answer from what they already believe, the platform asks about your brand by name, reads the answers, and flags wrong prices, old features and mixed-up identities, with the source behind each claim. An illustrative claim check on the site shows an engine reporting a price the brand retired last year and a free tier that does not exist, tracing it to a 2024 review page, and suggesting an update to the pricing page plus a request for the reviewer to refresh. Profile checks run on the sites engines lean on, and the platform audits crawler access — whether GPTBot, ClaudeBot and PerplexityBot can read you at all. When gaps are found, Proofsource ranks them by likely lift against effort, names the page or source to change, and drafts the fix from your own content for you to approve. The Product Hunt listing describes the final step: agents publish the approved changes, verify that the answers changed, and learn from every change. Proofsource describes one loop from the answer to the fix, summarised as Know, Act, Prove, Repeat. Most AI visibility tools stop at a score; Proofsource continues through what the engines say, why they say it, what to change, and whether the change worked. Onboarding follows a three-step path. First, you tell Proofsource your site; it reads it, works out your category and your competitors, and drafts the buyer questions worth tracking, and you approve every one. Second, the platform asks the engines those questions, keeping every answer with its sources and a screenshot. Third, you get the shortlist and the fixes: where you are named, who is named instead, which sources decided it, and the gaps worth closing first. The site also publishes a methodology page describing how it measures AI answers. For growth and brand leaders, Proofsource offers one number for AI visibility that can be defended in a board meeting, with the questions, engines and confidence range behind it. For writers and search teams, it supplies the questions you lose, the pages the engines cite instead, and drafts grounded in your own site — as the site puts it, search taught you to rank, this shows you how to be quoted. For agencies running many brands, the platform supports unlimited brands on one account, a weekly report per client, and a roll-up of who is winning and losing across the book. For founders and small businesses, the free trial gives 200 answers over two days with no card, so you can see in minutes whether ChatGPT recommends you and then fix the one page that matters most. Because each brand chooses how many questions it tracks, spend can be tied to the brands that earn their place. Concrete workflows follow directly from those roles. A founder can run a free scan before a launch, check whether ChatGPT names the company for its category questions, and start with the single highest-value page to change. A search or content team can look at the citation list, find a listicle that leaves the brand out, and pitch a listing or a correction on that cited page. A brand can run accuracy checks, discover that an engine is quoting a retired price from an old review, and draft a fix plus a reviewer refresh request. An agency can scan each client brand, send a weekly report per client, and roll up the results to see which brands are winning across its whole book. A growth lead can bring a mention rate with a confidence range, backed by the questions and engines behind it, into a board discussion about AI visibility. Proofsource tracks ChatGPT, Perplexity, Claude and Google AI Overviews on every plan, with Grok and DeepSeek available as add-ons on Custom plans. The site's logo strips also display Google AI Mode, Gemini, Microsoft Copilot and Meta AI. The free trial is described as 200 answers, 25 prompts per question, top AI engines, two days, free, and no card, with two scans — today and tomorrow. After that, custom plans start at $64 per brand per month, with the final price discussed on a demo call, and each brand chooses how many questions it tracks. The site also publishes side-by-side comparison pages against Profound, Otterly.AI, Peec AI, Semrush, Ahrefs, Scrunch, SE Ranking, Rankscale and AthenaHQ, covering engines covered, sampling cadence, statistics, fixes and price, with sources for every claim about another product. The shortlist for your category is being written right now by answer engines, and most brands never see it. Proofsource's value proposition is that it makes that shortlist visible — who is named, who is named instead, which sources decided it — and then keeps going with ranked gaps, drafted fixes drawn from your own content, and verification that the answers actually changed. Start free with 200 answers, 25 questions on every engine, and no card, and find out whether AI names your brand.

Oogwai Beacon is an answer engine optimization audit from Oogwai. It puts 20 real buyer questions about your category to the AI assistants your buyers use — none of the questions name your brand — and records how often each engine names your brand, who it names instead, and which sources it relied on to decide. The audit reports two halves separately: whether AI crawlers can read your site at all, and whether the engines actually cite you when they answer. It is built for the marketing, SEO and content teams of companies that sell into a category where buyers now ask an assistant for a shortlist rather than scanning a page of search results. The instant readability check runs on the page in seconds with no sign-up, and the citation report follows by email. The problem Beacon was built around is a change in buying behaviour. A buyer evaluating your category used to type a query, scan a page of results and form their own shortlist. Now they ask an assistant, and the assistant hands back a shortlist already formed — three names, a sentence each, and a follow-up question. Whoever is not in those three is not in the evaluation. Answer engines do not rank pages the way a search engine does. They assemble a reply from three things: what the model absorbed during training, what it retrieves live from the web at the moment of asking, and which of those sources it trusts enough to lean on. Your own website influences the first two weakly and the third barely at all — because when a buyer asks who the best vendor is, an engine will not settle the question by quoting the vendor. That is the gap answer engine optimisation works in: not keywords and rankings, but presence, corroboration and machine legibility, measured by whether you get named. Half one is readability, and it is pure HTTP with no engine calls — which is why the result lands on the page within seconds. It answers whether GPTBot, ClaudeBot, PerplexityBot and Google-Extended can reach your pages and find anything on them. The check covers robots.txt rules for each named AI crawler; whether the site serves server-rendered HTML rather than an empty shell filled in by JavaScript; Organisation, Product and FAQ structured data; llms.txt and a machine-readable summary of what you sell; heading structure, canonical URLs and clean sitemaps; and consistent entity facts — one name, one description. Because it is a technical pass, the fixes are usually a short engineering job, and Oogwai hands over the exact changes whether or not you go on to work with them. Half two is citation, and it is the part that requires actually asking the engines. Beacon puts buyer questions about your category to the engines in your tier. None of the questions mention your brand — because a question that names you proves nothing about whether you would be recommended. The report shows how often each engine names your brand unprompted, which competitors it names instead and how consistently, the sources each engine cited to get there, what the engines believe you do (correct or not), the question types where you are strong and where you vanish, and a ranked list of the sources worth being on next. That list is evidence from your own market rather than a generic media plan. Which engines run depends on tier. The free check queries ChatGPT and Gemini. The paid audit adds Claude, and Perplexity is covered on the managed programme, where regional variations and buyer personas are also tested; Copilot is covered on the managed programme as well. Claude grades markedly harder than the other two, so a two-engine result reads higher than a three-engine one — which is exactly why the AEO score is reported only on the paid audit, where it is measured against the full set. The free citation report — who ChatGPT and Gemini name instead — requires a free business-email account. Beacon's stated position is that AEO has two halves and only one of them is a technical problem. Most tools sell a single number, and a single number hides the distinction that matters, so Beacon reports the two halves separately. A perfect readability score with zero citations is common, and the audit treats it as the most useful result it produces: it tells you the problem is not on your website. Fixing robots.txt takes an afternoon; getting named takes presence on the pages an engine reaches for when it is asked to compare vendors — roundups, review platforms, comparison pages, community threads, editorial coverage, transcripts — almost never the vendor's own homepage. The Oogwai media and content team runs that work: source mapping from your own report's citations, editorial placement, review platforms such as G2, Capterra, Product Hunt, Clutch and TrustRadius, comparison coverage, community answers on Reddit, Quora, Stack Overflow and industry Slack and Discord recaps, video and audio transcripts, quotable owned content, entity consistency and monthly measurement. The engagement runs as four stages: Audit, Map, Place, Measure. The outcome for a user is a clear answer to two different questions. First, can AI crawlers read the site — delivered in seconds, free, with no sign-up and no credit card. Second, do the engines actually recommend the brand when a buyer asks — delivered as an emailed report that names the competitors being cited instead. Because the report records the exact domains each engine cited for your category, the follow-on work is targeted at sources that demonstrably feed AI answers in your market rather than at a generic media plan. Every gap the audit finds comes with the fix, and the technical changes are handed over whether or not you continue with the programme. Monthly re-runs of the identical question set let you see share of answers over time, which competitors gained or lost ground, and which placements moved the number, so the programme is judged on citations rather than impressions. Concrete scenarios the audit is built around: a B2B marketing team suspects competitors are being recommended by ChatGPT and wants the evidence, question by question. An SEO lead runs the free readability check in seconds to learn whether GPTBot, ClaudeBot, PerplexityBot and Google-Extended can reach the site at all, before any content work begins. A content or PR team takes the cited-domain list from a report and turns it into a ranked target list for editorial placement, review platforms and comparison pages. A company with a strong readability score but zero citations learns that the problem is off-site and points its investment at third-party presence instead of the website. And a team tracking a category month to month can see whether a placement moved a number weeks later, separately from what the model has absorbed on the far slower training cycle. Beacon is aimed at the marketing, SEO and content functions of companies that are bought through evaluation, and it is equally usable by teams that suspect they are already being named and need less work than they thought — the baseline is delivered before anything is promised. The free tier covers the readability check and the ChatGPT and Gemini question set. The paid audit adds Claude and carries the AEO score. The managed programme adds Perplexity and Copilot, regional variations and buyer personas, plus the media and content work. The readability score appears on the page in seconds; the full citation report is usually in an inbox within a few minutes and always within one business day. Free use requires no credit card. Beacon's proposition is narrow and specific: stop guessing what AI assistants say about your category and measure it. It separates machine legibility from citation, tests brand naming against unbranded buyer questions put to the engines themselves, and turns the citations it finds into a workplan — with the fix attached to every gap.