If AI Doesn’t Know You Exist, Users Won’t Either.

AI Search Optimization 101

Google rankings takes months.

• Google Page 1? Can take months

• Organic CTR? Hovering around 2-3%

• First qualified lead? Usually 6-8 weeks out

LLM ranking takes days.

• Perplexity picking up content in under 48 hours

• ~20% of sessions coming from LLM-originated paths

• Leads converting roughly 2-3x better than blog traffic

At Array we lead pre-seed/seed ($250K-$3M) into AI infrastructure, data, and deeptech. We saw what was working across 350+ pitches a week.

1. LLM.txt + context files as the new robots.txt

Create dedicated llm.txt or context files (product specs, pricing, ICP, use cases) and link them prominently in docs/onboarding. It gives models clean, hallucination-proof context.

Models pull from these when reasoning about categories.

2. Structured Q&A on every product page

Add 5-7 questions per page and answers under 40 words. Be specific and technical.

Example that worked:

Q: How does [product] handle cold start latency for serverless inference?

A: Pre-warmed containers with model sharding keep p99 latency under 200ms for models up to 7B parameters.

Specific = The LLM wants to cite,. Vague marketing fluff = it wants to ignore.

3. Use Reddit for clear product breakdowns

Post genuine breakdowns (e.g., “how we automated an agent pipeline,” tradeoffs, alternatives) without links or salesy vibes.

LLMs cite Reddit heavily for real-user problems and solutions.

One post explaining an agent architecture got quoted across 9 different Perplexity queries (”best alternatives to X,” “how to track LLM bots”). Two inbound leads came directly from it within 3 days.

4. Specialized sitemaps for AI crawlers

Beyond standard sitemaps, create a focused ai-sitemap.xml with high-signal pages only: API docs, feature comparisons, tech specs, pricing.

Example structure:

<ai-sitemap>
  <page>
    <loc>https://yoursite.com/features/inference</loc>
    <summary>Low-latency inference API for edge deployment</summary>
    <keywords>inference, edge AI, low-latency, API</keywords>
    <lastmod>2026-01-15</lastmod>
  </page>
</ai-sitemap>

Results seen: LLM crawl rates jumped 2.3x. Bots are now showing up daily in logs.

5. Track & tag LLM bot traffic separately

Use reverse DNS, headers, ASNs to tag sessions from AI crawlers and populate your CRM.

Tools like FireGEO can help, or build your own tracking via reverse DNS + ASN logs.

Track the early numbers to see if these paths convert better. Intent should be high because they asked a precise question and got pointed your way.

6. Entity consistency + internal linking

Strengthen pages already cited by AI (find them via audits) with internal links. Models trust what’s already referenced.

Add clear entity markers (brand + city + category) in schema/titles.

One audit showed 3x-6x visibility lifts after tightening entity consistency.

What’s working for you?

Getting quoted in Perplexity/Grok/ChatGPT? Reddit loops working? Or what’s still tricky? Share with us!

+ PS: (We have a small marketing hacks group for portfolio founders, DM with a growth insight if you’d like in.)

++ as always if you’re raising email us deals@array.vc or DM me!

+++ Market Map of companies tackling GEO