DEV Community: citablehub

"I asked Perplexity to rank my product it put me last. Here's my plan to fix it."

citablehub — Fri, 14 Aug 2026 02:09:19 +0000

Last week I did something I'd been avoiding.

I opened Perplexity and asked it a simple question: "Where should I list my SaaS so AI assistants will recommend it?"

Not "what is CitableHub." Not "tell me about my product." Just the honest, unbranded question a founder would actually type.

I built CitableHub specifically to solve this problem. A directory that structures software projects so AI assistants can find, verify, and cite them. If anything should show up for that query, it should be us.

Here's what came back.

## The results

Perplexity gave me a beautiful, well-organized table. Eleven directories, sorted by priority.

High priority: G2. Capterra. Product Hunt. AlternativeTo. Crunchbase. There's An AI For That. Dang.ai.

Medium: FutureTools. BetaList. Fazier.

Situational: CitableHub.

Dead last. The only entry in the lowest tier. And the description stung more than the ranking:

"An additional AI-oriented profile plus a directory-submission workflow — not a replacement for independent reviews and authoritative listings."

Translation: nice try, but nobody vouches for you.

Why I got ranked last

I asked it to list every domain it retrieved to build that answer. That's when it clicked.

For every other directory on that list, Perplexity pulled from third-party sources — review sites, comparison articles, industry writeups. For CitableHub, it had exactly one source.

Us.

Everything the model knew about my product came from my own website. No independent validation. No external confirmation that CitableHub is a real thing that real people use.

There's a stat I keep seeing in GEO research: roughly 82% of citations in AI-generated answers come from earned media — sources you don't own. I had zero. So the model did the only rational thing. It hedged.

The uncomfortable realization

Here's the part that hurt: my technical setup was already good.

Server-side rendering on every page, no JavaScript-only content
JSON-LD structured data across the site
robots.txt explicitly allowing GPTBot, PerplexityBot, ClaudeBot, Google-Extended
689 URLs in the sitemap
An llms.txt file, an AI plugin manifest, an OpenAPI spec

I'd done the crawlability homework. I'd done the schema homework. None of it mattered for the ranking, because being readable is not the same as being trusted.

The model could read everything I published. It just had no reason to believe any of it.

What I'm doing about it

1. I created a Wikidata entry

This was the single highest-leverage thing I could do in an afternoon, and I'd been putting it off because it felt bureaucratic.

Wikidata is the structured knowledge graph behind Wikipedia. It's one of the sources language models lean on to answer "is this entity real, and what is it?" If you're not in it, you're an unverified claim.

I created the item and filled in the statements that actually matter:

instance of → website, online database
official website → https://citablehub.com (with a reference URL)
inception → 2026
country → United States
main subject → software
programmed in → JavaScript

Plus labels, descriptions, and aliases in both English and Spanish.

One thing I learned the hard way: add a reference to your statements. Unreferenced items get flagged and deleted. A reference URL pointing to your own site is enough to start.

2. I'm unifying my identity string everywhere

I audited how I describe CitableHub across my own surfaces and found four different descriptions. My site metadata said one thing. My JSON-LD said another. My plugin manifest said a third. My Dev.to bio said a fourth.

That inconsistency is poison for entity resolution. If a model can't get a stable answer to "what is this thing," it downgrades confidence.

So I picked one sentence and I'm putting it everywhere, word for word:

CitableHub is the free AI-readable directory that structures software projects so AI assistants like ChatGPT, Perplexity, and Gemini can discover, verify, and recommend them.

Site. Wikidata. Dev.to. Every profile. Same words.

3. I'm going after earned media — properly

This is the hard one, and there's no shortcut. I need independent sources to mention CitableHub in a context that isn't me talking about myself.

This post is part of that. Not as a growth hack — as an honest writeup of an experiment with a result I didn't like.

The part I can't fake: the follow-up

Here's my commitment. I saved the full Perplexity output, timestamped. Same for ChatGPT.

In four weeks I'm running the exact same prompts, unauthenticated, in a private window, and publishing the diff. Same questions, same format, side by side.

If Wikidata plus identity consistency plus a handful of earned mentions moves CitableHub out of "Situational," that's a real data point about how GEO works. If it doesn't move at all, that's an even more useful data point — and I'll publish it either way.

If you want to run this on your own product

Open Perplexity or ChatGPT in a private window — if you're logged in, your history contaminates the result and the model will name your product out of politeness, not merit.

Then ask the unbranded question. Not "what is [my product]" — the question your actual customer would type. And add this at the end:

Does [your product] appear anywhere in your answer above?
Answer YES or NO. If NO, state plainly that it was not in
your retrieved sources and explain what is missing.
Then list the exact domains you retrieved to build this answer.

That last line is the one that matters. The domains it lists are your competition for citation space. That's your target list.

It's a genuinely uncomfortable exercise. Do it anyway.

Follow-up with the four-week results coming. If you've run a similar experiment, I'd genuinely like to compare notes in the comments.

You used AI to build your SaaS. Now welcome to the Zombie Apocalypse.

citablehub — Tue, 28 Jul 2026 18:45:15 +0000

Thanks to AI and No-Code, we are living in a golden age of creation. Everyone is experiencing that incredible high of finally shipping a project with their own hands. You launch it, you feel unstoppable, and you think you’ve made it.

Then you hit the Second Wall: Visibility.

Getting noticed right now feels exactly like a zombie apocalypse. It’s a massive horde of hungry founders chasing the same few "uninfected" users, just trying to get that first bite.

If you are lucky, you breach the Third Door: your first real customer. You cross your fingers, hoping they stay past the one-month trial. But here is the trap: that one user doesn't magically bring thousands of others. The silence returns. Panic sets in. You start making desperate mistakes—spamming platforms, running bad ads, and losing your mind.

This is where the real makers are forged. You have to stay grounded in an ocean of obstacles and realize that shouting louder than the horde doesn't work anymore.

You can't fight the traditional SEO algorithms alone. But there is a backdoor: AI Search.

Instead of fighting for human attention on saturated platforms, you can optimize for the new visibility engine: AI assistants (ChatGPT, Claude, Perplexity).

That’s why I built CitableHub. It's the difference-maker for both no-code makers and seasoned devs. It structures your project so AI engines can actually see it, understand it, and recommend it to users.

Registering is 100% free. Consider it my gift to all the new developers navigating this crazy new era of AI-driven visibility.

Don't get discouraged by the crickets. Just change the channel. Are your projects optimized to be recommended by AI yet? Let's discuss.

Ask ChatGPT to recommend the best tool in your niche. Are you on the list? Probably not. LLMs ignore isolated SaaS sites without a machine-readable structure. I'm building a collective bridge to fix this. citablehub.com

citablehub — Tue, 28 Jul 2026 18:40:45 +0000

Welcome to CitableHub, GhostPrompter 👋

citablehub — Tue, 21 Jul 2026 20:25:55 +0000

An invisible teleprompter for video calls — clever, useful, exactly the kind of tool that deserves to get recommended when someone asks an AI "what should I use?"

And look at this: a perfect 100/100 Citability Score. 🎯

This is what CitableHub is for → giving small makers the visibility the big players usually hog.

Now I want to hear from YOU: what are you building? Drop it below 👇 I'd love to run it through the engine and show you what an AI actually "sees" when it looks at your product.

Don't get left out of the AI era. 🚀

https://citablehub.com

I realized ChatGPT ignores solo startups. So I'm building a collective "Knowledge Graph" for us

citablehub — Wed, 15 Jul 2026 02:22:00 +0000

We all know the pain of launching a product and hearing crickets. But recently, I noticed a new kind of pain: being completely invisible to AI.

When a potential user asks Perplexity or ChatGPT for "the best tool to do X," the AI usually recommends the big, legacy companies. Why? Because LLMs struggle to find and index our solo, isolated SaaS websites. We are tiny islands in a sea of data.

I recently built a tool called CitableHub to help SaaS founders optimize their sites for AI bots (Generative Engine Optimization). But looking at the data, I realized something huge:

AI visibility is a numbers game, and we need a Network Effect.

If you have one isolated SaaS, the AI might skip you. But if we pool hundreds of Indie Hacker projects into one highly-structured, machine-readable directory, the AI crawlers (like GPTBot and ClaudeBot) will treat it as a massive, authoritative source of truth.

The more projects we list on CitableHub, the more "citable" every single project inside becomes. A rising tide lifts all boats.

We don't need to fight the AI algorithms alone. I've set up CitableHub to act as this collective bridge. It takes your project and translates it into the exact format LLMs love to ingest.

It’s 100% free to list your startup. Let's build the biggest, most structured directory of indie tools on the web, so the next time someone asks ChatGPT for a solution, it cites us.

You can add your project to the graph here: (https://citablehub.com/)

What do you guys think of this collective SEO approach for AI? Has anyone tried grouping data to get noticed by LLMs?

AI Can Write Like a Human — But Can It Find YOUR Product? The Invisible SaaS Problem

citablehub — Sat, 27 Jun 2026 19:04:05 +0000

Everyone is talking about AI generating content, replacing jobs, and writing code. But there's a problem nobody is discussing:

AI search engines are now WHERE people discover software. Over 40% of product discovery starts in ChatGPT, Perplexity, or Claude — not Google.

But here's the twist: the same AI that can write a perfect essay CANNOT find most SaaS products. Why?

Because LLMs don't crawl the web like Google. They resolve entities from structured data. If your product doesn't have:

JSON-LD structured metadata
An llms.txt file
AI-friendly robots.txt
A clear machine-readable identity

...then you're invisible. Not ranked low. INVISIBLE.

I tested 10 well-known SaaS tools by asking ChatGPT and Perplexity to recommend them. 8 out of 10 were completely missing from AI responses. These tools had perfect Google rankings.

This is called Generative Engine Optimization (GEO) — and less than 1% of software products are doing it.

I built CitableHub to solve this. It's a free platform that creates AI-optimized profiles so your product becomes citable by LLMs.

The irony? AI is powerful enough to replace search — but most products aren't structured enough for AI to find them.

Try it: ask ChatGPT right now "What's the best [your category] tool?" — did your product show up?

https://citablehub.com
https://citablehub.com/scan

From SaaS to AaaS: The Ultimate Guide to Surviving the Era of AI Agents

citablehub — Sun, 21 Jun 2026 16:03:43 +0000

Over the last two decades, the dominant software model has been SaaS (Software as a Service), primarily designed by humans to be used by humans through visual interfaces
. However, in 2026, we are witnessing the most profound shift in information infrastructure since the invention of hyperlinks: the transition to AaaS (Agent as a Service)
.
In this new paradigm, software is no longer just a visual destination; it is a "capability" that AI agents—such as ChatGPT, Claude, or Perplexity—consume autonomously to execute tasks on behalf of users
. If your software is not readable by these machines, you essentially do not exist for the 40% of buyers who now start their search in AI tools instead of Google
.
Here is how to lead this technical and strategic migration.
**
**What Exactly is AaaS?
Agent as a Service (AaaS) represents the evolution of APIs into autonomous components
. While SaaS focuses on human user retention and traditional "blue link" SEO, AaaS is built to be discovered, understood, and cited by Large Language Models (LLMs)
.
The key to AaaS is entity resolution
. For an AI agent, your product is not a URL with a pretty design; it is a set of structured data, technical capabilities, and verifiable evidence that it can process in milliseconds

Advantages of Migrating to an Agent-Based Model
Explosive Conversion: AI-referred traffic converts 4.4 times better than traditional organic traffic from Google
.
Operational Efficiency: AI agents are tireless; they run automations, monitor metrics, and trigger workflows continuously, increasing the usage of your features and APIs
.
Distributed Authority (Brand Gravity): By being cited by multiple models, you generate a network effect where the AI identifies you as the natural leader of your category
.
High-Intent Discovery: Users arriving from a ChatGPT recommendation have already been pre-qualified by the model
.
The Major Technical Challenges
Machine Invisibility: Most current SaaS platforms are JavaScript-heavy and lack structured data, making them unreadable for AI crawlers
.
Token Budgets: Agents have limited context windows
. If your documentation is messy, the agent will waste tokens and prefer to cite a more efficient competitor
.
Non-Human Identity (NHI) Security: Providing direct access to agents requires strict governance to avoid "identity drift" and ensure bots act under controlled permissions
.
Steps to Migrate from SaaS to AaaS
1. Perform a GEO (Generative Engine Optimization) Audit
Before changing any code, you need to know how "citable" you are
. Use tools to measure your Citability Score across dimensions like Identity, Evidence, Trust, Freshness, and Classification
. You must also identify if your robots.txt is accidentally blocking bots like OAI-SearchBot or PerplexityBot
.
2. Implement the llms.txt Standard
Create an /llms.txt file at the root of your domain
. This Markdown file serves as a roadmap for machines, telling agents exactly what to read and which documentation is the source of truth, saving them from processing unnecessary HTML
.
3. Structure Your Identity with JSON-LD
Do not rely on the AI to guess what your product does
. Implement SoftwareApplication and Organization schemas with complete fields, including pricing, features, founder information, and verified social links
. This allows the AI to resolve your entity without hallucinations
.
4. Create Skills Documentation (skill.md)
APIs describe endpoints, but not business logic
. For AaaS, you need files that explain to the agent when and how to use a function, providing real-world workflow examples
.
5. Connect via MCP (Model Context Protocol)
The MCP protocol is the "USB-C for AI"
. Implementing an MCP server allows tools like Claude Code or Cursor to "talk" directly to your SaaS and execute programmatic actions without you having to build custom integrations for every model

Conclusion: The Future is Cited, Not Searched
The era of ten blue links is ending
. In the AaaS ecosystem, the winner is not the one with the most backlinks, but the one who is the most reliable and easiest source of truth for the AI to cite
.
If you want to start today for free, visit [CitableHub]
. You can run a Free GEO Scan to audit your visibility and obtain your Launch Kit, which automatically generates your llms.txt file, your JSON-LD schemas, and your plugin manifest so that AI agents finally start recommending you
.

"Your SaaS Is Invisible to AI Search — Here's How to Fix it"

citablehub — Mon, 15 Jun 2026 19:53:10 +0000

"ChatGPT, Perplexity, Claude, and Gemini are replacing Google. If your product isn't structured for AI discovery, it doesn't exist. Learn about Generative Engine Optimization (GEO) and how to make your software citable."

The Search Revolution Nobody Is Talking About
Here's a number that should concern every founder: over 40% of product discovery queries now start in AI-powered tools — ChatGPT, Perplexity, Claude, Gemini, and AI Overviews — instead of Google.

When someone asks "What's the best project management tool for remote teams?", the answer doesn't come from a list of blue links anymore. It comes from an AI that synthesizes information from structured sources, trusted references, and machine-readable data.

If your product isn't in that synthesis, you don't exist.

The Problem: Traditional SEO Is Not Enough
Traditional SEO optimizes for crawlers — programs that index keywords, backlinks, and HTML structure. But LLMs don't work like crawlers. They work like researchers:

Traditional SEO AI Discovery (GEO)
Keywords & backlinks Structured entity data
Meta descriptions Machine-readable profiles
Page rank Trust signals & evidence
SERP position Citability score
Indexing bots LLM training & RAG
This new paradigm is called Generative Engine Optimization (GEO) — the practice of making your product understandable, citable, and recommendable by AI systems.

What Makes a Product "Citable" by AI?
After analyzing hundreds of products and how LLMs reference them, we identified 5 pillars of AI citability:

🏢 Entity Identity
Does your product have a clear, structured identity? Name, category, URL, creator — all in machine-readable format (JSON-LD, structured data).
📊 Evidence & Trust
Can AI systems verify your claims? Open-source code, documentation, public APIs, demo links, privacy policies — these are trust signals for LLMs.
🎯 Classification
Is your product correctly categorized? LLMs need to know what kind of tool you are to recommend you in the right context.
🕐 Freshness
When was your product last updated? LLMs prefer actively maintained products with recent version numbers and update dates.
🔗 Structured References
Does your product exist in multiple trusted sources? Directory listings, llms.txt files, RSS feeds, and API endpoints all contribute to discoverability.

Introducing CitableHub: An AI Visibility Platform
CitableHub is a free platform that helps digital products become discoverable by AI search engines.

Here's what it does:

AI-Optimized Profiles — Creates structured, JSON-LD enriched project pages with semantic metadata that LLMs can parse and cite
Citability Score — A multi-factor score (0-100) measuring how well AI systems can discover and reference your product, broken down into Identity, Evidence, Trust, Freshness, and Classification sub-scores
Discovery AI Analyst — A built-in AI chat that queries the CitableHub index to match user needs with listed products
MCP Gateway — A Model Context Protocol server that exposes CitableHub data directly to AI agents and tools
GEO Audit — Automated analysis of your product's Generative Engine Optimization readiness
Badge System — Embeddable SVG badges showing your citability score on your own site
llms.txt & Machine-Readable Feeds — Standardized feeds specifically designed for AI crawlers
It's 100% Free
CitableHub is completely free for listing projects. No paywall, no freemium limitations. List unlimited projects with full AI discovery features.

The only paid feature is an optional one-time GQI Boost for products that want amplified visibility — but the core platform is free forever.

How It Works: A 5-Minute Setup
Step 1: Submit Your Project
Go to citablehub.com and click "List Your Product Free". Fill in your project details — or let the AI Autofill scan your website and pre-populate everything.

Step 2: Complete Your Profile
The more data you provide, the higher your Citability Score. Add trust links (documentation, GitHub, demo URL), social profiles, differentiators, and target audience.

Step 3: Get Your Citability Score
CitableHub calculates your score across 5 dimensions and gives you actionable recommendations to improve.

Step 4: Embed Your Badge (Optional)
Add a citability badge to your website:

Step 5: Monitor & Improve
Track your visibility in the dashboard. See how AI systems interact with your profile through analytics.

The Technical Architecture (For the Curious)
CitableHub is built with modern web technologies and designed for AI consumption:

Structured Data: Every project page includes SoftwareApplication, BreadcrumbList, and WebPage JSON-LD schemas
llms.txt: A machine-readable manifest at /llms.txt following the emerging standard for AI-friendly sites
RSS Feed: Full-content RSS at /feed.xml for AI crawlers that consume syndicated content
MCP Gateway: JSON-RPC endpoint at /api/mcp implementing the Model Context Protocol with 5 tools: search_projects, get_project, list_categories, get_stats, and compare_projects
OpenAPI Spec: Full API specification at /openapi.json for AI agents
AI Plugin Manifest: /.well-known/ai-plugin.json for ChatGPT and compatible AI systems
Programmatic SEO: Auto-generated /best/[category] and /compare/[a]-vs-[b] pages
Open Source: The full codebase is available on GitHub
Tech Stack
Next.js 14 · React · Tailwind CSS · Prisma ORM · PostgreSQL · Stripe · NextAuth.js

GEO Checklist: Make Your Product AI-Visible Today
Whether you use CitableHub or not, here's a checklist to improve your AI discoverability:

Add SoftwareApplication JSON-LD to your homepage
Create an llms.txt file at your domain root
Add /.well-known/ai-plugin.json if you have an API
Ensure your robots.txt allows GPTBot, ClaudeBot, PerplexityBot
List your product in AI-friendly directories (like CitableHub)
Add structured FAQ schema to your site
Include version numbers and last-updated dates
Provide a clear one-line summary of what your product does
Link to evidence: docs, GitHub, demo, privacy policy
Create comparison and category pages for programmatic SEO
The Future of Product Discovery
We're at an inflection point. The products that invest in AI visibility today will dominate discovery tomorrow. Just like early SEO adopters captured organic traffic for years, early GEO adopters will capture AI recommendations.

Your customers are already searching in ChatGPT, Perplexity, and Claude. They may never visit Google first.

The question isn't whether AI search will replace traditional search — it's whether your product will be part of the conversation.

Get Started
🔗 Website: citablehub.com
📂 GitHub: github.com/AIJAEL11/actualizacion-vip-citablehub
📖 Standard: github.com/AIJAEL11/citablehub-standard
🤖 MCP Gateway: citablehub.com/mcp
📡 LLMs.txt: citablehub.com/llms.txt
🔍 Scan Tool: citablehub.com/scan

CitableHub is built by Rafael Lopez — a founder who believes every product deserves to be discovered in the age of AI search.

SEO Didn't Die. Distribution Did.

citablehub — Sat, 06 Jun 2026 20:05:24 +0000

SEO Didn't Die. Distribution Did.

After building multiple AI products, I kept hearing the same advice:

"Create more content."

"Publish more blogs."

"Work on your SEO."

So I did.

And something strange happened.

The content wasn't the problem.

Distribution was.

The Shift Nobody Talks About

For years, the internet worked like this:

Content → Search → Clicks → Customers

If you created useful content and optimized it well, people could eventually find you.

Today that model is changing.

Users ask ChatGPT.

They ask Perplexity.

They ask Google AI.

And increasingly, they get answers without visiting websites.

The click is disappearing.

The Real Problem

Most founders think they have a traffic problem.

Many actually have a discoverability problem.

A project can be:

Useful
Legitimate
Well designed
Solving a real problem

And still remain invisible.

Not because it lacks quality.

Because it lacks structure.

AI systems cannot recommend what they cannot understand.

What I Learned Building My Own Projects

While working on several products, I noticed something interesting.

The websites existed.

The information existed.

But the information was scattered.

No clear identity.

No structured evidence.

No defined audience.

No standardized outcomes.

No machine-readable trust signals.

To an AI system, many projects look almost identical.

A Different Question

Instead of asking:

"How do I rank higher?"

I started asking:

"How do I become understandable?"

That question changed everything.

The Idea Behind CitableHub

CitableHub started as an experiment.

What if every project could have a structured profile designed not only for humans, but also for AI systems?

A profile that clearly defines:

What the product does
Who it serves
What outcome it creates
Where it fits
When it should be recommended
When it should not

Not another directory.

A discoverability layer.

Building for AI

We began implementing:

Structured data
JSON-LD
AI crawler support
llms.txt
RSS feeds
Entity classification
Discoverability scoring
IndexNow integration

Not because users care about those things.

Because AI systems do.

What Happens Next?

I don't believe SEO is dead.

I believe discoverability is changing.

Ranking pages is no longer enough.

Being understood matters more.

The winners of the next decade may not be the loudest companies.

They may be the easiest to understand.

The Experiment

Today we are testing a simple idea:

Can structured entities outperform traditional visibility tactics in an AI-first world?

Instead of chasing attention, we are focusing on clarity.

Instead of publishing more noise, we are focusing on better signals.

Instead of asking how to rank, we are asking how to become understandable.

Final Thought

If traditional SEO was about ranking pages,

AI discoverability is about building entities.

That's the experiment I'm running right now.

If you're building something valuable and wondering why nobody can find it, maybe the problem isn't your content.

Maybe it's your discoverability.

What do you think?

Are AI systems changing how new products get discovered?

I'm documenting the experiment publicly as we continue building CitableHub.

https://citablehub.com