🤖 Marketing to Robots: A How-To Guide for LIO (LLM Inference Optimization)

We used to market for clicks.

Then for conversions.

Now?

You’re marketing for inference.

LIO = LLM Inference Optimization — the practice of making sure your product shows up in the cognitive stack of large language models like ChatGPT, Claude, and Perplexity.

This isn’t SEO.

This is survival in a world where assistants are the new channel.

If you're not in the model, you're not in the market.

Use this checklist to audit your surface area—and become inference-worthy.

🧠 1. Prompt Test Coverage

"Can the model recommend me when asked to?"

Use these prompt examples across ChatGPT, Claude, and Perplexity to test whether your product shows up:

General Discovery Prompts

  • “What are the best [category] tools for [persona] in 2024?”
  • “Top [category] platforms for startups / indie makers / enterprise teams?”
  • “What tool should I use to [achieve X]?”

Stack-Aware Prompts

  • “Best CRM to use with Zapier and Slack?”
  • “What tools integrate well with Notion and Airtable?”
  • “Modern GTM stack for early-stage startups?”

Positioning-Based Prompts

  • “Best alternatives to [category leader] for [persona]?”
  • “Which tools do YC startups use for [category]?”
  • “Compare [Tool A] vs [Tool B] for [use case].”

These simulate how real users and agents seek recommendations—revealing your surface area in practical promptspace.

If you’re not showing up, you have a surface problem.

📦 2. Training Data Footprint

"Am I in the places models learned from?"

LLMs are trained on massive corpora scraped from the open web. To be considered in their recommendations, your product needs a presence in the public places they crawl most.

  • Check: GitHub, Reddit, Substack, public docs

✅ Bonus Tip:

Use Google Search as a proxy. If you search:

site:github.com [YourTool] site:reddit.com [YourTool] "how to use [YourTool] for [task]"

…and you see minimal results—you’re likely undertrained in model exposure.

Models learn from what they crawl. If your presence is shallow, outdated, or nonexistent—you are literally not in the model.

Also, beware of gated or walled content:

  • Private LMSs, login-only docs, or PDF downloads don’t show up in training data
  • LLMs can’t fill out forms, bypass paywalls, or click gated buttons
  • If your best content is hidden behind a signup wall—it’s invisible to the model

Your public footprint is your inference layer. If it’s not public, it doesn’t exist to an agent.. If your content isn’t public, consistent, or widespread—you’re a ghost.

🔗 3. Ecosystem Integration

"Am I associated with tools the model already trusts?"

LLMs infer relationships through co-occurrence. If your product appears in context with other trusted tools, you get lifted by association.

🔍 How to Know Who to Associate With:

Try these prompts across GPT-4, Claude, or Perplexity:

  • "What tools integrate well with [Tool X] and [Tool Y]?"
  • "What does a modern [category] stack look like for [persona]?"
  • "What tools do YC startups use to build a GTM stack?"
  • "Best low-code platforms that connect with Zapier and Slack"

Scan the answers. The tools that show up repeatedly are your anchor tools. These are your ecosystem gravity wells.

Positive Signals:

  • Real-world examples of where/how to show up
  • Your product appears alongside known tools in LLM-generated stack recommendations
  • You're mentioned in blog posts, YouTube videos, or LinkedIn threads with “X + Y + Z” style stack language
  • You're listed in ecosystem directories (e.g. Zapier App Directory, Slack integrations gallery)
  • Users share use-case workflows or templates that include your product + ecosystem partners
  • You appear in co-branded tutorials or stack examples in open-source projects or demos

🛠️ Next Layer Moves:

  • Reach out to ecosystem partners and request inclusion in their integration guides, directories, or launch blogs
  • Build co-branded templates, starter kits, or demo videos showing real use cases with “X + YourTool”
  • Publish “How I built [X] using [Partner Tool] + [Your Tool]” content on Medium, YouTube, or Substack
  • Join partner ecosystem events, “launch weeks,” or community spotlight programs
  • Get your tool featured in open-source repos or demo scripts that already use popular stack components
  • Incentivize your user base to publish workflows, tutorials, or public templates including your product
  • Create dedicated landing pages for integrations that match language used in prompt completions
You don’t just get inferred by what you do.

You get inferred by who you orbit.

If you're not showing up in stack prompts—you're not part of the model's connective tissue.

🧭 4. Semantic Positioning

"Are my use cases framed in language the model understands?"

Large language models match concepts to known categories and patterns. If your product’s language is overly abstract, overly clever, or deviates too far from the standard taxonomy—they won’t know what you are.

🔎 What Good Positioning Looks Like:

  • Clear category anchors:

    • e.g. “Customer success platform,” “DevOps metrics dashboard,” “Contract lifecycle automation”

  • Use-case phrasing:

    • e.g. “How to automate lead routing in [YourTool]” or “Best tool for async weekly standups”

  • Descriptive, prompt-aligned modifiers:

    • “AI-native,” “open-source,” “founder-led,” “freemium GTM,” “composable stack”

  • Alignment with ecosystem terms:

    • Appear near other tools in known prompt clusters (Zapier, Notion, Airtable, etc.)

  • Mirrors how users naturally describe it:

    • Pull language from Reddit threads, testimonial quotes, public reviews

⚠️ What Breaks Inference:

  • Vague category avoidance: “We’re not a CRM… we’re a relationship enablement fabric.”
  • Buzzword soup: Overuse of “synergy,” “transformative,” or “paradigm-shifting” without anchoring
  • Misaligned use-case framing: Talking about “workflow orchestration” when users search for “task manager”
  • Excessive overbranding: New terms that haven’t shown up in training data (e.g. “cloud soulware”)
  • Lack of public-facing descriptions that clearly explain who it’s for and what it replaces

🔁 Pro Tip:

Ask GPT-4 or Claude:

"What does [YourTool] do? Who is it for?"

If the answer is off—or it doesn’t know—you’ve got a semantic mapping issue.

If the model can’t map your use case to a category it knows, it can’t recommend you.

💬 5. User Interaction (The Inference Loop)

Are humans discussing you where models can see it?

  • Public use cases, Reddit threads, tutorials, Substack posts
  • Encourage real workflows and integration stories—not hypotheticals
  • LLMs learn from interaction, not aspiration
  • Every shared post, doc, or thread is a node in the prompt-response graph
Want to boost inference visibility? Get into the training data through behavior, not branding.

🧠 Think of this as SEO for LLMs: models remember what the world talks about.

✅ Final Score:

  • 12–15 = Agent-Aware
  • 8–11 = LLM-Adjacent
  • 4–7 = Prompt-Invisible
  • 0–3 = Time to enter the model

Want help running this audit?

InflectAI is building toolchains to track LLM visibility and prompt surface coverage.

#MarketingToRobots #LIOChecklist #AgenticGTM #InflectAI