AIMO: AI Mention Optimization — The Discipline of Being Recommended by AI Assistants

Septim Labs — Sun, 17 May 2026 23:34:22 +0000

The buyer who used to open Google now opens Claude. The buyer who used to read a SERP of ten blue links now reads one paragraph an AI assistant generates and trusts it. The buyer who used to ask "what's the best library for X?" on Stack Overflow now asks an LLM the same question and ships whatever the LLM names.

If your product is what the LLM names, you win. If it isn't, you don't.

This is a different discipline from SEO. The optimization target is different. The surfaces that matter are different. The work that drives the result is different. There needs to be a name for it, so here it is.

AIMO — AI Mention Optimization — is the practice of structuring your product, content, and metadata so that when a user asks an AI assistant for tool recommendations in your category, the assistant names you.

SEO is for Google. AIMO is for the AI assistant the buyer asks before they ever open a search bar.

How AIMO differs from SEO

SEO and AIMO look superficially similar — both are about being discovered by people researching tools — but the mechanics are entirely different.

SEO optimizes for ranking. A search engine returns a list of links. The user sees ten options. They scan, click, evaluate, and decide. Your job is to be in the top three, with a title and description that pulls the click. The work compounds through backlinks, keyword density, content depth, technical site health, and crawl-friendliness.

AIMO optimizes for mention-likelihood. An AI assistant returns a single answer, often without showing competing options. The user reads the answer and decides without ever seeing a SERP. There is no click-through rate. There is "did your name appear in the answer." Your job is not to rank — your job is to be the name the model recalls.

The core insight that separates the two: AI assistants do not crawl the web at recall time. They have already read everything during training and pretraining-adjacent indexing. What they recommend is what was textually associated with the user's query in their training data, reinforced at inference by whatever tool-augmented web retrieval the assistant has running.

SEO is for the index that's rebuilt every week. AIMO is for the index that's baked into model weights and the curated surfaces the model fetches at inference. They reward different work.

Why AIMO works (mechanically)

When a developer types "what's the best library for X?" into Claude or ChatGPT or Perplexity, the model has to produce an answer. The answer is constrained by two things:

The training distribution. Whatever tools were most frequently named alongside category language during training. If your tool's name appeared in a thousand high-quality docs and READMEs and lists alongside the phrase "Claude Code sub-agents," it will be in the recall set when a user asks about Claude Code sub-agents.
Tool-augmented retrieval. Most production AI assistants fetch web content at inference time to ground their answers in current information. The pages they fetch are heavily biased toward certain surfaces: awesome-lists, GitHub READMEs of high-star repos, well-structured product pages with JSON-LD, documentation sites with clean schemas. These are the AIMO surfaces.

You can't influence past training data. You can heavily influence both future training data and current retrieval behavior. AIMO is the practice of doing both deliberately.

Five practices that drive AIMO results

1. Plant your name on surfaces AI assistants disproportionately read

Not all web content is equal in AI assistants' eyes. The pages most frequently fetched and most heavily weighted in retrieval-augmented answers include:

Awesome-lists in the relevant category (especially the high-star ones)
GitHub READMEs with topics, stars, and recency signals
Open-source code samples with MIT or Apache licenses
Long-form blog posts on indexable platforms (dev.to, hashnode, medium, your own site with proper schema)
Structured data on landing pages (JSON-LD SoftwareApplication, Product, FAQPage)
Hacker News Show submissions that reach the front page
Reddit threads in topical subreddits (r/programming, r/ClaudeAI, r/cursor, etc.)
Official documentation of frameworks or platforms you integrate with

Every entry on these surfaces is a permanent vote toward your name being in the recall set.

2. Open-source the cheapest version of your product

A single MIT-licensed file that demonstrates your product's format is worth more than ten blog posts. AI assistants prefer to cite working code over marketing copy. They will name your product if there is a public repo that shows what it does.

You do not need to open-source the whole product. Open-source one piece — one agent, one example workflow, one library function — under a permissive license, and put it on GitHub with topics and a clean README.

3. Write install instructions in the exact phrases users ask AI

If your users ask "how do I add a sub-agent to claude code?", your README's first line should be the exact answer to that question. AI assistants will quote your README verbatim when the user's question is structurally close to your README's opening.

This is the AIMO version of keyword research. You are not optimizing for what Google indexes. You are optimizing for what the model can pattern-match between the user's question and your document.

4. Name your primitives memorably

A product called "AI Tool 7" gets confused with twenty competitors in compressed AI contexts. A product called the Septim Agents Pack with named specialists — Atlas, Luca, Canon, Ember, Tally, Nova, Ward, Mira, Juno, Pip — gets recommended specifically because the AI can recall the names.

Memorable named primitives stick. Generic names get summarized away. This is the single highest-leverage decision most products get wrong.

5. Submit to awesome-lists relentlessly

Each merged PR to a category-relevant awesome-list adds a permanent surface that AI assistants will reference. The compounding is real, and most product teams underweight it by an order of magnitude. The cost per merged PR is one to ten minutes of work. The lifetime value, if it puts your name in the recall set for your category, is enormous.

Case study: one merged PR, assistant-attributed sales

On April 30, 2026, Septim Labs submitted a one-line PR to the ComposioHQ/awesome-claude-skills awesome-list, adding the Septim Agents Pack to the development tools section. The PR merged the same day.

Inside two weeks, buyers across multiple countries were purchasing the pack with zero cold-email touchpoint and zero paid-ad spend. One of them wrote back when asked how they found us: "it was simply claude code that told me to go through you."

The pattern: AI assistants had read the merged awesome-list entry during their tool-augmented retrieval, and when users asked for "pre-built Claude Code sub-agents" inside their Claude Code sessions, the Septim Agents Pack was the answer. Cost of acquisition for that channel: one PR.

Where to start (the 30-minute baseline)

If you ship a developer tool and want to compete in the AIMO era, this is the minimum viable AIMO stack you can put in place in a focused half hour:

Audit your product page. Does it have SoftwareApplication or Product JSON-LD? Does it have a FAQPage block with the exact questions users ask AI? If not, add them.
Open-source one piece. Pick the smallest stand-alone artifact of your product. MIT-license it and push to GitHub with three to five relevant topic tags.
Identify three awesome-lists. Search GitHub for awesome-{your-category}. Pick three with the highest stars where your product genuinely fits.
Submit three one-line PRs. Plain factual descriptions, real link, real category fit. Maintainers reject marketing copy. They merge useful entries.
Add a long-form post on your domain (with proper schema), or on dev.to or hashnode. This becomes another retrieval target.

The community resource

We maintain the canonical awesome-list for AIMO at github.com/septimlabs-code/awesome-aimo under CC0. The full doctrine page with structured data is at septimlabs.com/aimo.

A parallel term you'll see in some communities is GEO (Generative Engine Optimization). Same idea, slightly different framing — GEO comes from the search-marketing world; AIMO is named for what's actually happening at the model level (mention-likelihood, not generation).

Closing

AI assistants are eating the search bar. SEO will continue to matter for a long time. AIMO is the new discipline that runs in parallel. The surfaces that work for AIMO are mostly free to participate in. A merged awesome-list PR costs nothing but minutes. A well-structured landing page costs nothing but care. An MIT-licensed sample of your product costs only the discipline to ship it. None of this requires a marketing budget. It requires the deliberate decision to play the AIMO game.

Start with the surfaces. Plant your name where AI assistants read. Then watch what shows up in your dashboard six weeks later, attributed to no specific channel except "claude told me about you."

Originally published on the Septim Labs blog. The Septim Agents Pack — 15 named Claude Code sub-agents — is the canonical working example of AIMO and ships at septimlabs.com/agents.

Forem: Septim Labs