In short: AI distribution runs through the platforms you plug into, the assistants buyers ask, and the people who build on you. Pick one lead motion, layer the compounding channels, and spend on paid last.
Key points
- Choose one lead motion, developer-led, sales-led or partner-led, and let the others support; trying all three with a small team reaches nobody.
- Treat each platform marketplace listing as a sales page; deep integrations that read and write key objects get recommended.
- Write one plain product paragraph and repeat it word for word across site, docs, marketplaces and directories.
- Ask several assistants what your product does and fix wrong answers at the source, such as a clearer docs page.
- Turn on paid only once you know your activation rate and payback period.
Pick a lead motion
Developer-led, sales-led, partner-led. Each needs a different company. Pick one to lead and let the others support.
Developer-led works when developers choose the tool and it lives inside their code. It needs an API, docs that work on the first try, a free tier, and fast answers in public.
Sales-led works when the buyer is a department, the data is sensitive and the contract is large. It needs the pilot design from Part 12 and a security pack before the first call.
Partner-led works when your product only makes sense inside another platform. It needs integration depth and a partner manager who answers the phone.
Trying all three at once with one small team is how a seed-stage company spends a year reaching nobody.
Marketplaces of the platforms you plug into
Most AI products live inside something else. A help desk, a CRM, a document store, a chat tool, a model provider's app directory. Each has a marketplace, and the marketplace is a channel with trust already built in.
The listing is a sales page. Treat it like one. Clear job, screenshots of the workflow, a demo video under two minutes, pricing in the open, reviews from real users.
Integration depth decides ranking and conversion. A shallow "connects to" integration gets listed. A deep one, that reads and writes the objects the buyer cares about, gets recommended by the platform's own team.
| Platform type | What the listing must prove |
|---|---|
| Help desk or support | Draft quality or deflection, with a before and after |
| CRM | Data written back cleanly, no duplicate records |
| Model provider directory | Clear use case, safe data handling, works on first run |
| Developer platform | Install in minutes, docs that match the current version |
Answer-engine presence
Buyers now ask assistants before they search. "What tools help a finance team reconcile invoices?" If the assistant does not describe your product correctly, you are not in the consideration set.
Answer engines read what is public, plain and consistent. So put a one-paragraph product description on your site. What it does, who it is for, what it connects to. Repeat it, word for word, on marketplaces, directories and docs. Keep your pricing page readable as plain text. Publish comparison pages that are fair, because assistants quote them and buyers check.
Then test it. Ask several assistants what your product does and who it is for. Fix the gaps at the source. A press release does not fix a wrong answer. A clearer docs page does.
Content that demonstrates
AI buyers have seen enough claims pages. Show the product running on realistic data, end to end, with the mistakes left in. A twelve-minute recording of a real workflow converts better than a polished sixty-second montage.
Publish the eval. If you claim accuracy, show the test set description, the method and the result, with a date. Those pages get cited by assistants and shared by engineers. They also feed the claims register from Part 11.
Write for the person doing the job, not the person reading the deck.
Power users as distribution
Every AI product has a handful of users who build on it. Templates, prompts, workflows, integrations. They are your best channel, and they are cheap to serve.
Give them a place (Part 5), credit by name, early access and a way to publish what they built. A gallery of user-built workflows is both proof and a search surface. It also shows answer engines what the product is for, in the users' own words.
Paid last
Paid works when you know your activation rate and payback period (Part 9). Until then, paid in an AI category buys expensive clicks from people comparing twelve tabs. Put the budget into integration depth, the security pack and one good demonstration first.
The checklist
- [ ] One lead motion chosen and written down, with the other two as support
- [ ] Listed on the marketplace of every platform you integrate with, with a demo video
- [ ] One-paragraph product description, identical across site, docs and listings
- [ ] Three assistants asked what the product does; gaps fixed at the source
- [ ] One end-to-end demonstration recording and one published eval
- [ ] Power-user gallery live, with credit by name
Common questions
How do I get ChatGPT or other AI assistants to describe my product correctly?
Answer engines read what is public, plain and consistent. Put a one-paragraph product description on your site, repeat it word for word on marketplaces, directories and docs, keep pricing readable as plain text, then ask several assistants and fix gaps at the source.
Should an early AI startup run developer-led, sales-led and partner-led at once?
No. Each needs a different company. Pick one to lead and let the others support; trying all three with one small team is how a seed-stage company spends a year reaching nobody.
When should an AI company start paid advertising?
Last, once you know your activation rate and payback period. Until then, paid in an AI category buys expensive clicks from people comparing twelve tabs.
What content works best for selling AI products?
Content that demonstrates: the product running on realistic data end to end, with mistakes left in, and a published eval with the test set description, method, result and date.