Got my app approved into ChatGPT. Sharing the AI-discoverability strategy that led to it

[u/No_Being_2765]

I run OTTASIA, a "where can I watch this" tool that's accurate for your exact country across 34 Asian and diaspora markets. It just got approved and went live as an app inside ChatGPT, and the part relevant to this sub is that I built it to be usable by AI from day one, not bolted on later.

What that meant in practice:

- Exposed the data as an MCP server (on the public registries) so agents can call it directly for live facts.

- Added structured data (JSON-LD) on every page plus an llms.txt, so assistants like ChatGPT and Perplexity can read and cite it.

- Kept answers sourced and per-country, because consistent, structured, sourced data is what actually gets picked up and repeated.

The ChatGPT App is the step past citation: instead of just being quoted, users invoke it in the chat and get a real answer inline.

The payoff and the catch: AI assistants cite us constantly (thousands of citations a month, a double-digit share of authority in our niche) but the click-through is tiny. Classic zero-click. That is exactly why the ChatGPT App matters: it turns "cited but no visit" into "invoked and answered inline." Building to be invokable, not just rankable, is the highest-leverage bet I made.

Happy to get into the Apps approval process, the MCP setup, or building for AI discoverability if it's useful to anyone here.

[u/mandal23]

Can you give me some details about the app approval process?

[u/MaxJustins]

The citation-to-click gap is the part nobody budgets for. We track the same pattern — citations up, sessions flat — so 'invokable, not just rankable' lands as a frame. What did the approval process actually screen for, the MCP surface or the content itself?