Launching today
sitefire.ai
Marketing suite for the agentic web
254 followers
Marketing suite for the agentic web
254 followers
sitefire (YC W26) is the marketing suite for the agentic web. We don't just monitor - we act: sitefire agents analyze what content drives citations, write brand-aware articles, and push to your CMS (Framer, Webflow). sitefire agents also surface PR outlets and UGC that influences AI answers, and provide tailor-made outreach suggestions. Save yourself a SEO and content person, and use sitefire to start marketing to agents.
sitefire.ai
Hi Product Hunt! I'm Jochen, co-founder of sitefire (YC W26). 👋
My co-founder Vincent and I met at TU Munich, and have backgrounds in software engineering and reinforcement learning from Stanford. We started sitefire in late 2025 after becoming convinced of one thing:
Websites are going away. Going forward, people will interact with brands via AI agents like ChatGPT and OpenClaw. This means brands need to design their marketing content for AI agents, not just humans.
The problem? Most AI visibility tools stop at monitoring. They show you dashboards but don't help you actually do anything. We wanted to build the tool that takes action for you.
How sitefire works:
For every topic where you want to be visible, our AI agents analyze what content drives AI citations - top-cited pages, query fan-out, sourced domains, and more. Then, sitefire recommends one of four actions:
📝 Create content - Fully-written, brand-aware, AI-optimized articles based on top-cited pages, your sitemap, and SERP data. Push to your CMS (Framer, Webflow) in one click.
✨ Improve existing pages - Our AI agents know your sitemap. If you have content that could be cited but is not, we suggest tweaking it so LLMs are more likely to cite it.
📣 Earn media - See which PR outlets drive AI answers for your topics. Get research on how to approach them, incl. the publisher contact and email draft.
💬 Engage in communities - Find high-value Reddit threads and other forums that matter, with suggestions on what to post.
What's new today:
Starting today, every sitefire plan includes AI-optimized articles and one-click CMS publishing. We went from "here's what you should do" to "we did it for you.". This is our hello world moment.
Our product is free to try for 7 days. You can set up your account in 5 minutes and start getting your first content recommendations.
👉 We'd love your feedback: What's the biggest challenge you face with AI visibility for your brand?
Please tell us what is still wrong with our product. How can we make it better for you?
Thank you for your support! 🙏
Timebox.so
@jochenmadler Killer product!
sitefire.ai
@mahendrakerr thanks!
Told
Curious how you handle the quality control loop when agents push content directly to a CMS — that's where I'd get nervous. Auto-publishing brand-aware articles sounds good until one goes out that's slightly off-tone and you're doing damage control. The citation analysis piece is the most interesting part to me, because understanding what content actually influences AI answers is still pretty murky for most teams. We've been thinking about this at told.club from a different angle — what users say in feedback often ends up being the raw material that shapes how a brand gets described, and that gap between company-published content and user language is huge. Would love to know if you're pulling in any of that signal or just working from existing indexed content.
sitefire.ai
@jscanzi Our customers currently do a final review of the draft. We don't auto-publish quite yet. But we can check what they changed before publishing each time. And we our agent can reflect on that to refine the context over time.
On your user review angle: do you know any good studies on this? Would be super cool to look at some data!
The thesis that brands need to optimize for AI agents and not just humans is going to age well. Most marketing teams are still thinking about this as an SEO problem when it's really a completely different distribution channel. The action-oriented approach over dashboards is the right call. Nobody needs another monitoring tool that tells them they have a problem without fixing it. How are you thinking about the feedback loop when LLMs update their citation behavior? What works today might not work in 3 months.
sitefire.ai
@devon__kelley That's a great question!
This was a big problem in SEO. Google changes the algorithm, a bunch of strategies stop working or turn negative.
We think everything will converge on good content. That's what the models try to estimate. You can optimize short term and "overfit" on their current objective-function. But you have to keep a balance. If you overfit too strongly you will inevitably run into issues.
The fact that this changes on a regular basis and that your competitors also keep competing in this zero-sum game makes a solution like sitefire so important. You need to see when it happens and a way to update all of that content.
Interesting launch, @jochenmadler! Congrats to you and @vincent_jeltsch1.
What stood out to me is the loop you’re closing. Sitefire analyzes what gets cited. Then generates brand-aware content. Then pushes it directly to the CMS.
That “research → content → publish” flow is powerful.
One question while reading through the page.
When your agents analyze citations across models like ChatGPT, Gemini, and Perplexity, do you see different citation patterns between them?
Or do the same types of sources tend to appear across models?
I'm excited to see how this evolves. Great launch.
sitefire.ai
@jochenmadler @taimur_haider1 Great question!
We see that the citation rate of content is different across models. This boils down to the search index used in the background. When you prompt ChatGPT or Gemini:
The model translates your prompt into 10-20 "fan-out" search queries. Those searches are different for each model and much longer than a human google search. That's the first source of difference.
Those are being run on the index. ChatGPT mostly uses Bing. Gemini uses the Google index. The second source of differences.
At the end of the day, each model wants to cite good content. That's what we strive for, while also optimizing on the way.
@jochenmadler @vincent_jeltsch1 Thanks for the explanation, Vincent!
It’s interesting to see how model-specific citation strategies affect visibility. I’d like to see how sitefire measures which content performs best across models over time.
sitefire.ai
@jochenmadler @taimur_haider1 Answering what content performs best across models is an interesting question. There are good studies from some of our analytics-focussed competitors on this (e.g. Reddit-bias).
Generally how it works: we run hundreds of "probing prompts" every day to collect answers. These probing prompts aim to be realistic questions your customers would ask. And then we extract all relevant data.
Additionally we are working on network-log integrations which will be super interesting to add to the mix!
@jochenmadler @vincent_jeltsch1 Got it. The network-log integration will be really powerful.
I’ve been thinking about how some of these insights could impact homepage visibility.
I’m sharing a few of these observations on LinkedIn.
Would like to hear your take when you have a moment.
How does sitefire identify the specific content features—such as structural patterns, entity relationships, or citation density—that successfully trigger citations from diverse AI answer engines like ChatGPT, Gemini, and Perplexity?
sitefire.ai
@mordrag LLM answers are a multi-step process and so it's important to break it down a bit:
Every major model runs background searches, so called fanout queries on a search index. ChatGPT uses Bing, Gemini uses Google. Perplexity have their own index as well.
This doesn't mean your SEO performance translates. They don't just search for your prompt. They come up with 10-20 really long fanout queries. Example from our own data: "geo-analytics tools for tracking AI search visibility 2024 2025". Nobody optimized their SEO performance for that.
Once the search results are available, the model looks for good content that it can trust. Authority, statistics, solid sources, much of that. This is where structural patterns play a role.
So how do we do it? We look at the content that does each step well. For steps 1-2 we run SERP on the fanout queries and analyze that content. For step 3 we look more closely at the snippets that were actually extracted. This is done by agents.
And of course the overarching optimization is making sure we don't recommend blog posts when the topic is being driven by editorial content instead. That's why we have 4 types of actions.
Great work! Congrats on the launch!
Quick question: Would you say Sitefire already makes sense for smaller or low-authority websites, or is it mainly useful once a site already has some domain authority?
sitefire.ai
@juliuswunderlich We have many customers who start their blog with us and who have done little for SEO before. And we do see results for them. But setting a solid technical foundation, which overlaps with SEO quite a bit is still important.
We are working on providing actions for technical improvements as well since that can become a real blocker.
On authority & backlinks: we don't have a feature for directly helping our customers win backlinks. But creating good content is the first step for that!
sitefire.ai
@orateur You onboard, which just means you select topics your customers care about and connect your CMS.
Then this is the current flow:
Log in once a week to pick click a "diagnose" button on topics you want to improve in
Tackle 1-3 of the actions, e.g. push two blog posts to framer, outreach to one journalist.
Review & set a publish date in Framer.
But: we will build a Slack agent so you can manage it all from there or MCP. Not quite there yet.