I made an LLM visibility tool that doesn't suck
LLM Eagle is an AI visibility optimization tool for SaaS, built for developers and indie hackers.
Why This Problem
I took on this project not just because:
The GEO (Generative Engine Optimization) industry is booming
VCs are funding similar startups
The idea is validated
Product-market fit is proven
But it's also because I am genuinely excited for this under-explored opportunity.
The Tech Stack
I used:
React with Remix/React Router V7
TypeScript
Tailwind with DaisyUI (I discovered DaisyUI and fell in love)
PostgreSQL with Neon
From Simple to Unavoidably Complete
It started with a simple plan: a rudimentary, minimally viable program that provided a basic GEO report once you entered your domain.
Nothing too shiny, just something that works.
However, as is often the case with MVPs, I kept building.
I fell into the "feature creep" trap - continuously iterating without feedback, stuck in an endless cycle of building and repeating.
Multiple buronouts, sleepless nights, headaches, mindlessly coding and adding features.
This time however, I didn't regret the cycle as much as I usually would. Because that extra work resulted in a more complete product that is actually useful. Since I knew the idea was already validated, I was comfortable putting more effort into the MVP before hitting the market.
The Product
So what features did I actually build?
You can add multiple websites to optimize them for GEO.
There are 4 main sections:
Overview - Visibility and sentiment of your website
Prompts - What prompts are being asked by your target audience
Content - Recommended article titles to maximize reach (you can generate articles as well)
Competitors - Top performers in your industry and their strength
I am iterating on this structure as I get feedback.
The Marketing
I’m still trying to figure out what works. I am currently finding my niche and dialing in my outreach methods by experimenting across X and LinkedIn.
If you are checking the product out, do comment here or message me at scorder96@gmail.com with feedback. I'd really appreciate it!
Product Link: llmeagle.com
Thank you for reading.
Really cool project! 👏 LLM Eagle gives a clear sense of being a powerful AI exploration and insight tool that “big picture” name sets a strong expectation from the start. The minimal design and focused layout make it feel approachable, which is important given how complex AI tooling can feel to users.
One area I’m curious about is how you’re guiding first-time visitors toward understanding exactly what they can achieve with LLM Eagle especially those who might not immediately know the ideal use case. Offering a quick visual or narrative example of a successful outcome (e.g., “See patterns in your docs in under 60 seconds”) could help bridge that gap.
Keen to hear about the early feedback you’ve been getting on both the concept and the first-visit experience!
That's a really good suggestion! Right now, I'm mostly doing the guiding as part of the marketing. The product itself definitely lacks refined copywriting. I'm planning to work on that once I better understand which audience resonates with the product and how they’ll use it.
There's little feedback, but it has led to several improvements. For example, I'm trying to make the website feel less AI-generated (it's not vibe-coded). I've added deep tracking for prompts and am also working on the UX, especially focusing on clarity and making the product much more intuitive to use.
Good to hear from you
Did you manage to get any sign ups?
Hi Ali, yes I have a few!
Congrats on the launch, looks solid. How are you currently thinking about acquiring early users and gathering feedback?
Thank you! I post on X and do outreach on LinkedIn every day. I'm also planning to start cold emailing soon. Perhaps by the end of it, I will publish another post about what worked in marketing.
That’s solid, consistency on X and LinkedIn will definitely help long-term.
If you’re planning a cold email too, one thing that could speed things up is tapping into communities where your exact users already hang out. For example, Reddit can work really well for early traction if approached the right way (not spammy, just value-first).
If you’d like, I can share a simple way to test it without spending much or risking your brand.
Interesting space. GEO for LLM visibility is still early, so tooling there makes sense.
I like that you moved beyond a single report and built multi site tracking plus prompts and competitor views. That’s closer to something teams could actually use.
One security question:
Since users add their domains and you analyze prompts, competitors, and potentially generate articles, how are you handling outbound requests and third party model calls?
• Are all model API keys strictly server side with no exposure to the client?
• If you fetch or crawl user provided domains, are you protecting against SSRF, internal IP access, and redirect abuse?