Hi HN! We're Vincent and Jochen from sitefire (https://sitefire.ai). Our platform makes it easy for brands to improve their visibility in AI search.We’ve been working together for years and have backgrounds in RL/optimization at Stanford and software engineering. We came to this idea after speaking with marketing teams who were seeing declining traffic due to Google’s AI Overviews and didn’t know what to do.This space can feel esoteric. Many case studies, few actual studies. Constant battle against myths (e.g. you need a llms.txt vs. you don't need a llms.txt) and "GEO hacks". We try to be more data-driven. And we try to be more bold and build a system that not only monitors, but actually improves traffic from AI search.While Google performs a single search, AI search engines expand the user prompt into 3-10 fan-out queries. The sourced pages are ranked using a classified algorithm similar to Reciprocal Rank Fusion (RFF). Finally, the LLMs skim the pages and decide what snippets to cite. Our goal is making sure brands have the right content that makes it through this funnel.Here is how sitefire works:- The user defines a set of prompts they want to monitor. These are synthetic prompts - we generate them based on SEO keywords and their monthly search volume.- We submit these prompts to ChatGPT, Gemini, Google AI Mode, etc. on a daily basis and capture the answers. We extract fan-out queries, sourced pages, citations, and brand mentions.- For each topic, our agents analyze which web pages are sourced and cited the most, and why. They also consider similar pages that you already have.- Based on the diagnosis, our content agents draft improvements or create new pages, and push them directly to the client’s CMS.- We integrate with the client’s network logs and Google Analytics to monitor the increase in AI bot requests and human referrals to their page.This system is continuously updated, so it always shows which content works, and how
FL score
out of 100
Verdict
high confidence
Competition
24
competitors found, nascent market, funded players
Trend
8 community mentions
An AI-powered platform for brands to automatically optimize and publish content for better visibility in AI search overviews.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Sitefire (YC W26) – Automating actions to improve AI visibility”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
This idea addresses a critical and growing pain for brands struggling with AI search visibility, offering a promising automated solution in a competitive but evolving market. The main hurdle for a solo builder is the significant complexity and resource demands of the technical implementation.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market demand and excellent timing, but high build complexity and a crowded competitive landscape temper the overall score.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Addresses a clear problem with good monetization potential, but the technical complexity and ongoing maintenance requirements make it less suitable for a solo builder aiming for simplicity and high leverage.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear value proposition for a specific audience, but faces significant assumption risks, complex distribution, and a challenging validation path for a micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a real and urgent pain for a specific persona, but the initial wedge might be too broad given the technical complexity and dynamic market.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, nascent market
AI visibility monitoring and optimization platform for brands in AI search results.
Pricing: unknown
Monitors how often your brand appears in AI tools like ChatGPT, Perplexity.
Pricing: unknown
AI Visibility Monitoring tool.
Pricing: unknown
Free tool scoring company visibility in LLMs like ChatGPT, Claude, Gemini across prompts.
Pricing: free
GEO platform to monitor and improve visibility across ChatGPT, Perplexity, Google AI.
Pricing: free 14-day trial
Monitors AI search visibility.
Pricing: unknown
GEO focused platform to track and improve AI citations.
Pricing: unknown
SEO + GEO monitoring with managed optimization.
Pricing: unknown
Offers AI brand visibility tracking as part of its comprehensive SEO suite.
Pricing: Minimum monthly price: $165. Maximum monthly price: $455. AI Toolkit starts at $99/month/domain/subuser with an annual plan.
Includes 'Brand Radar' for AI visibility tracking, with deeper analysis as an add-on.
Pricing: Minimum monthly price: $108. Maximum monthly price: $374. Brand Radar add-on: $199/month.
One of the most accurate and user-friendly AI visibility software solutions, offering full-spectrum tracking.
Pricing: Minimum monthly price: $79. Maximum monthly price: $284. Core: $189/mo; Plus: $355/mo; Max: $519/mo.
Affordable and straightforward tool for monitoring and improving brand visibility across multiple AI platforms.
Pricing: Minimum monthly price: $25. Maximum monthly price: $422. Lite plan: $25/month (billed annually); Standard plan: $160/month.
Gaps they leave open
What people say, 8 mentions
I made a lot of mistakes with my first SaaS startup. I'm sharing them here along with the solutions I have in mind – are they correct?
r/SaaS
Claude now runs my entire website SEO and content strategy. My mind is genuinely blown.
r/SaaS
Top 5 AI Agents for SaaS Customer Support in 2026
r/SaaS
AI Content Campaign Got 4M impressions, Thousands of Website Views, Hundreds of Customers for About $100 — This is the future of marketing
r/Entrepreneur
I’ll use your SaaS for free + give detailed feedback (UX, bugs, growth ideas)
r/SaaS
Online estate planning business struggling to make sales
r/Entrepreneur
How do you automate collecting actionable customer feedback for product development?
r/SaaS
The Hidden Challenges of Scaling Enterprise Support (And How to Tackle Them)
r/Entrepreneur
Recent news
Google confirms AI headline rewrites test in Search results
Search Engine Land, March 20 2026
Over The Top SEO Launches Dedicated Generative Engine Optimization Division, Becoming One of the First Agencies to Offer Full-Service AI Search Optimization
Markets Insider, March 16 2026
AI Search Optimization Best Practices Every Brand Needs in 2026
openPR.com, March 16 2026
Over The Top SEO Launches Dedicated Generative Engine Optimization Division, Becoming One of the First Agencies to Offer Full-Service AI Search Optimization
MarTech Series, March 17 2026
Top 4 AI Visibility Agencies for Well-Funded Startups Ready to Win AI-Driven Discovery
Current Issues and Topics - For Small Businesses and Entrepreneurs, March 15 2026
Market signals
The market for AI visibility and Generative Engine Optimization (GEO) is rapidly expanding, with numerous startups and established SEO players offering solutions to help brands appear in AI-generated answers, driven by a shift in consumer search behavior towards AI platforms and a predicted decrease in traditional organic search traffic.
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI