Who it's for
Growth marketing for AI startups, executed.
Every AI startup makes the same claims, so claims no longer differentiate. What still works is demonstrated specifics: what your system does that a reader can verify, where it fails, and what you are willing to show. Zway finds those specifics inside your product and publishes them on your channels every week.
Last reviewed 27 August 2026
The bottleneck
The problem with how AI startups usually do this.
The category is impossibly crowded and every competitor makes the same claims in the same eight words, so positioning language has stopped carrying information. A buyer reading your homepage has read forty like it this quarter and has learned to discount all of them on sight. The only thing that still separates you is a demonstrated specific the reader can check, and most AI startups have several sitting in their engineering channel and publish none of them.
The claims layer is saturated
Open ten homepages in your category and the copy is interchangeable. Fast, accurate, enterprise-ready, human in the loop, built on frontier models, trusted by teams. Nothing in that sentence is false and nothing in it is information.
This is not a failure of taste. It is what happens when thousands of companies describe genuinely similar capabilities using the same vocabulary at the same time. The result is that buyers have stopped reading the claims layer entirely and skip to whatever looks checkable.
So the question is not how to write better claims. It is what you can put on the page that a competitor cannot copy the next morning.
Specifics are the only thing left that reads as true
The test is simple: could a competitor paste this sentence onto their own site without changing anything? If yes, it is doing no work.
| What everyone writes | What only you can write |
|---|---|
| Powered by advanced AI | Which models, which fine-tune, on what data, and why that choice |
| Ten times faster | The benchmark, the hardware, the baseline, and the run you can point at |
| Enterprise-ready | The deployment options, the audit trail, the largest workload you have actually run |
| Human in the loop | Exactly which decisions a human makes and what happens when they disagree |
| Highly accurate | Your evaluation set, your metric, and the three cases where it fails |
The right column is harder to write, requires an engineer for twenty minutes, and cannot be produced by a model that has never seen your system. That is the whole reason it works.
Where your buyers actually look first
Your audience uses assistants more heavily than almost any other buying population, and they use them at the shortlisting stage rather than the browsing stage. Someone asks for the best options in your category, gets four names, and picks from those four. If you are not one of them, you never see the impression, the click, or the loss.
Getting cited is a slower and more structural problem than ranking. It requires content that exists, is indexed, is specific enough to be worth quoting, and states things plainly enough to be extracted. Vague positioning copy is close to unciteable, which is another reason the specifics matter.
What we run
Weekly, on your accounts. A technical piece built from a real evaluation or a real failure case. Founder posts in the same register, drafted from your own words. Comparison pages for your category written by you rather than by an affiliate site. Human operators participating in the communities where your buyers argue about this category, under their own identities, not as anonymous accounts pretending to be users.
The part we will not do
We will not manufacture social proof. No invented benchmarks, no borrowed logos, no reviews we arranged. In a category where every third claim is inflated, the fastest way to lose a technical buyer is to be caught once, and it is not recoverable. If the specific is not there yet, we publish the ones that are and say nothing about the rest.
Channels that work here
- AI search visibility, because your buyer asks an assistant first
- Technical long-form built around evaluations and failure cases
- Founder-led posting in specifics rather than category language
- Hacker News and developer communities, as a participant
- Comparison and alternatives pages you write before someone else does
The first thirty days
What a first month looks like for a AI startups company.
| Week | Focus |
|---|---|
| Week 1 | Founder and engineering interview. Extract the specifics: what the system does, on what data, and where it breaks. |
| Week 2 | Audit what the assistants currently say about your category and who they cite. Map the gap. |
| Week 3 | First technical piece published on a real evaluation or failure case. Founder posting begins in the same register. |
| Week 4 | Comparison pages drafted. Operators active in the communities where your buyers argue about this category. |
Questions at this stage
Everyone in our space says the same thing. How do we sound different?
Is it safe to publish where our model fails?
Why does AI search matter more for us than for other startups?
Our product changes every few weeks. Does content go stale?
Can you write technical content without an engineer involved?
See the plan for your company.
Thirty minutes. You leave with a written thirty-day plan either way.
30 minutes. You leave with the plan either way.