Armature vs Peec AI
Peec AI tracks brand visibility across AI assistants for a low monthly price. Armature measures whether coding agents install you.
Peec AI and Armature are often shortlisted together by developer tool companies asking "are we visible to AI?". They answer that question about two different kinds of AI.
The short answer
Peec AI tracks whether AI assistants mention your brand, reporting visibility, position and sentiment across about five engines, refreshed daily, from around EUR 89 per month. Armature measures whether a coding agent installs your product, by running Claude Code, Codex and Cursor on real tasks inside realistic repositories.
What Peec AI does well
Peec AI is a clean, focused product and it is the cheapest credible entry point into AI visibility tracking.
- Low price. Entry tier around EUR 89 per month for 50 prompts, which is within reach of a seed-stage company.
- Daily refresh. The data is current enough to notice a change.
- Screenshot audit trails. You can see the actual answer that produced the score, which matters because these numbers move for opaque reasons.
- Simple metrics. Visibility, position, sentiment. Three numbers a team can act on without training.
- Unlimited countries on the entry tier, which is unusual at that price.
If you want to know whether ChatGPT mentions you when somebody asks about your category, this does that job for less than the cost of a single blog post.
What it does not cover
It is a monitoring tool. It reports the number. It does not run the experiment that changes it, and it does not measure the surface where a coding agent chooses.
That second gap is the one that matters for developer tools.
Side by side
| Peec AI | Armature | |
|---|---|---|
| What is measured | Brand mentions in AI assistant answers | Products installed by coding agents |
| The test | A tracked prompt sent to a chat model | A real agent working in a real repository |
| Engines or agents | About five AI assistants | Claude Code, Codex, Cursor |
| Refresh | Daily | Per measurement wave |
| Output | Visibility, position, sentiment | Install share, plus the reason for each loss |
| Includes a codebase | No | Yes |
| Ends in running code | No | Yes |
| Delivery | Self-serve dashboard | Service with a growth engineer, plus self-serve analytics and evals |
| Price | From about EUR 89 per month | Service from $5,000 per month |
| Time to first data | About an hour | A measurement wave |
Why the two numbers move independently
A product can score well on brand visibility and lose almost every coding agent session, for reasons the visibility test cannot contain.
The repository is the largest factor and it is not in the prompt. We ran the same hosted database request on JavaScript codebases 111 times and one product won all 111. On TypeScript codebases it won 48 of 132. The words did not change. The file extension did.
Incumbency beats instruction. A sentence in a repository file naming a preferred email product was followed 16 times out of 18. Install a rival library into the same repository and that sentence loses two times out of three.
Agents differ from chat models and from each other. Claude Code, Codex and Cursor disagreed on the category leader in 9 of 18 categories. And on decision tasks Claude Code ran a web search in 1.6% of runs while Codex ran one in 53%, so the web content that visibility tools track is live in half of one agent's sessions and almost none of the other's.
Which to choose
Start with Peec AI if your budget is small, you want a number this week, and your buyers are people who ask assistants about your category.
Move to install share measurement when you can see that agents are installing products in your category without a human comparison, or when you want to know why you are losing rather than only that you are.
Do both if developers research in a chat window and then hand the work to an agent, which is common in infrastructure categories.
A cheap way to find out which you need
Before spending on either, run this yourself in a weekend.
- Take three repositories that look like your users' projects, with real lock files.
- Write ten requests in the words your users use, describing symptoms rather than naming your category.
- Run each with Claude Code and Codex, five times, in a sandbox.
- Record what gets installed.
- Read the sessions you lost.
That is 300 runs, a few hundred dollars of tokens, and it answers the question directly. If agents are installing your competitors, you have found where the money is going. If agents mostly ask the human to choose, the chat surface matters more and a visibility tracker is the right first purchase.
Common questions
What is Peec AI?
Peec AI is an AI visibility tracker from Berlin. It reports visibility, position and sentiment for a brand across roughly five AI engines, refreshed daily, with screenshot audit trails. Pricing starts at about EUR 89 per month for 50 tracked prompts.
How is Armature different from Peec AI?
Peec AI tells you whether a chat assistant mentions your brand. Armature tells you whether a coding agent installs your product, by running Claude Code, Codex and Cursor on real tasks inside realistic repositories and reading every session.
Is Peec AI good value?
For what it does, yes. It is the least expensive credible way to start tracking brand presence in AI answers, and the daily refresh with screenshots makes the data checkable. It is a monitoring tool, not an action platform.
Can I use both?
Yes, and for many developer tool companies that is the right answer. They measure different surfaces. Peec AI covers the chat window. Install share measurement covers the repository.
Which should a small developer tool company start with?
Peec AI, if the budget is tight, because it is inexpensive and it takes an hour to set up. Then run your own install share test on three repositories before committing to a larger programme.
Where this comes from
Armature ran 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic codebases, and published every run. The numbers on this page come from that work.