How to get your tool recommended by ChatGPT and Gemini

First: check your current score

Before you start changing things, know where you stand. Open ChatGPT, Gemini, and Perplexity and run 3–5 buyer questions for your category. Write down:

• Whether your product is named at all
• Where it appears in the list (position 1 is very different from position 6)
• Which competitors appear instead of you
• Whether the answer varies significantly between models

This is your baseline. Everything below is aimed at moving these numbers.

Step 1: Nail your one-sentence positioning

AI models match products to buyer questions using category signals. If your product is described differently across your landing page, your README, your GitHub description, and third-party mentions, the model sees inconsistency and defaults to competitors that are clearer.

Write one positioning sentence and use it everywhere:

[Product name] is a [category] for [audience] that [key differentiator].

Examples:

• "ColorDev is a color tool for developers that generates accessible palettes with one-click contrast checking."
• "Viestro is a public product hub for indie developers that tracks visitors, collects followers, and checks AI visibility."

This sentence should appear verbatim (or close to it) in your: landing page H1 or subtitle, GitHub repository description, README first paragraph, product directory listings, and your Viestro public page tagline.

Step 2: Write FAQ content with real buyer questions

AI models are trained to give direct answers to questions. If your website or docs contain the exact question someone is asking — as a heading — and a clear answer that mentions your product, you're in a much better position to be cited.

Format that works:

## What's the best tool for generating accessible color palettes?

ColorDev generates WCAG-compliant color palettes automatically,
with contrast ratios shown for every color combination.
You can export to Tailwind, CSS variables, or JSON.

Add 5–10 FAQ entries on your public page or a standalone FAQ doc. Use the questions your buyers actually type — not generic Q&A filler. Good sources for real questions: Reddit threads in your niche, Quora, the "people also ask" section in Google results, and Perplexity's "related questions."

Step 3: Get mentioned on sources AI trusts

AI models weight mentions from certain platforms more heavily than others. In rough order of impact:

1. GitHub (stars, README, repo description) — especially for developer tools. A well-starred repo with a clear README is one of the strongest signals.
2. Hacker News (Show HN posts, Ask HN comments) — highly indexed, heavily cited, and trusted by every major model.
3. Reddit (especially r/sideprojects, r/SaaS, r/webdev, r/indiegaming) — community discussion creates multiple mentions across threads.
4. Product Hunt — a "Featured" badge or a top-10 daily finish creates an indexed product page that AI models cite.
5. Comparison and listicle articles — a single "10 best color tools for developers" article that includes you creates lasting signal. Guest posts, reaching out to bloggers in your niche, or getting listed on alternative-to sites all count.
6. Your own public changelog — active, dated updates signal that the product is maintained. AI models learn from recency.

You don't need all of these. Picking two or three and doing them well is enough to move from invisible to visible in 2–4 months.

Step 4: Maintain a public changelog

An active, public changelog does two things for AI visibility: it signals that the product is actively maintained (a key trust factor), and it creates fresh indexed content about your product at a regular cadence.

What to include:

• What changed, in plain English (not "fix #243")
• Why the change matters to users
• When it shipped

Even one update per month is enough. The goal is a product page that shows "last updated N days ago" rather than "last updated 8 months ago."

Step 5: Build a public page with structured data

A public product page with proper metadata — title, description, category, features, and JSON-LD schema — is indexed faster and cited more reliably than a GitHub repo or a landing page with heavy JavaScript (which many AI crawlers still struggle to render).

The page should have:

• A clear, keyword-rich title and meta description
• A features section that uses concrete, specific language
• An FAQ section (see Step 2)
• A regularly-updated changelog
• JSON-LD markup for SoftwareApplication or WebApplication schema type

Step 6: Check and iterate

AI visibility isn't a one-time fix. Run your buyer questions again every 4–6 weeks and look at what changed. Specific things to watch for:

• Did you move from "not mentioned" to "mentioned" for any question? That's a win — the tactic that preceded it is working.
• Are you consistently mentioned on one model but not others? Means your presence is indexed somewhere that specific model weights heavily.
• Did a competitor's position drop while yours improved? Useful data on relative momentum.

Typical timeline: zero mentions → first mentions within 6–12 weeks if you do Steps 1–4 consistently. Reaching score 60+ from zero takes 3–6 months of steady work.

What doesn't work

A few things that get suggested but don't move AI visibility:

• Keyword stuffing your landing page. AI models read for meaning, not keyword density. Clear language beats repetition.
• Paid ads. No evidence that ad spend influences what AI models recommend.
• Buying backlinks. Traditional SEO tricks don't translate to AI recommendation systems.
• Social media follower counts. Twitter/X presence matters much less than forum discussions and documentation quality.

Related guides:

• What is AI visibility and why your startup needs it
• How to launch on Product Hunt
• Best Reddit communities for indie hackers