Building Niche AI: 3 Lessons From Selling to Game Devs

I run Ziva, an AI agent that lives inside the Godot editor. We picked a niche on purpose: instead of competing for general-purpose AI coding share with Cursor, Copilot, and the rest, we built a tool for the 5% of game devs who use Godot. That decision saved us. Other decisions almost killed us.

If you are building something for a niche audience, especially in AI, here is what I would tell a past version of myself.

1. The niche is not the audience. The niche is the substrate.

When we said "we are the AI for Godot," people heard "we are competing for the Godot audience against other plugins." That is not what we meant. The Godot ecosystem is too small to be a long-term moat. What it actually gives you is a substrate other AI tools cannot reach.

GPT-4, Claude, and the other big LLMs were trained on a lot of GDScript that is wrong. There is roughly 10x more Unity C# than GDScript on the open web, so when a frontier LLM tries to write GDScript, it averages out toward Unity-flavored patterns. Variable signatures look right but call into APIs that do not exist. Methods get mixed between Node and Node2D. Autoloads get treated like Python singletons.

We can fix this in ways the general-purpose tools cannot, because we know exactly which engine version the user has, what nodes are in their scene, and what the compilation errors say. None of that information lives in a generic chatbot.

The lesson: when you pick a niche AI market, you are not just picking a customer pool. You are picking the part of the world where general AI is structurally weak, and the structural weakness is what you sell against.

2. Distribution stops mattering in the wrong order

For the first three months I obsessed over distribution. Where do Godot devs hang out? r/godot. The official Discord. GMTK Game Jam streams. I made content for each one and tracked clicks.

The clicks did not convert. The reason was not the channels. It was that the product had not crossed the threshold where a click becomes a user. People landed on ziva.sh, got curious, downloaded the plugin, and then ran into the same paper cuts that the AI itself was supposed to fix.

What worked was the opposite order: fix the product until first-session retention is good, then turn distribution back on. Once we got first-session retention from "tries it once and never again" to "tries it once and uses it the next day," every channel that had been dead suddenly worked.

If you are building a niche AI tool, the temptation is to ramp distribution before retention is solid. Resist it. The TAM in a niche is small enough that you cannot afford to burn audience on a leaky funnel. You will not get a second shot at the same person.

3. Open-source-adjacent products have specific marketing rules

Godot is open source. Most of our users contribute to or follow open-source projects. That changes how marketing has to feel.

Things that work in this audience:

• Honest comparison content. Our most-cited blog post compares us to a free open-source AI plugin and does not pretend we always win. We win where it matters; we say so where we lose.
• Technical depth. A 1,500-word post on a specific Godot quirk performs 10x better than a 400-word product update.
• Showing your work. Open-sourcing parts of the stack, posting build logs, contributing fixes back to Godot itself. All of these compound trust in ways generic SaaS marketing does not.

Things that absolutely do not work:

• "Boost your productivity by 10x" copy. The audience smells corporate AI marketing from a kilometer away.
• Influencer posts. Most Godot YouTubers will turn down sponsorships even when offered, because their audiences will revolt.
• Aggressive Reddit posting. r/godot has a brutal anti-promo norm. One borderline post and you lose the channel forever.

The rule we landed on: every piece of marketing has to read like it could have been written by someone who is not selling anything. If a Godot dev would resent the post if they realized it was sponsored, do not run it.

What I would do over

Two specific tactical decisions I would change:

I would have shipped a free tier on day one. We launched with a paid trial because we wanted to validate willingness to pay. The free tier we eventually added was the single biggest growth lever we have ever pulled. Validating willingness to pay is what investor decks are for. Validating product-market fit is what free tiers are for.

I would have built the Discord earlier. Our Discord is now where bug reports, feature requests, and most casual conversations happen. We launched it nine months in. The users who were around for the first few months mostly churned because there was nowhere to talk to other users about the product. A live community is a retention feature, not a community-building feature.

Where we are now

We are six months into the current chapter and the product is working. The growth is not Substack-headline-worthy yet, but the unit economics are clean and the channel mix is sustainable without paid ads.

The biggest single insight has been that the AI tool space rewards specificity. Generic agents are commoditized. Domain-specific agents that can outperform GPT-5 on a specific stack still have room to win. That is the bet. We will see how it plays out over the next year.

If you are building in a niche AI space and want to compare notes, I am at ziva.sh. Always happy to swap data with other founders solving similar problems.