AGI House · The Living Internet · Oct 10, 2026

AgentRank

Get recommended by the AI agents deciding where buyers go — no traffic required.

A simulator of AI answer engines, a bandit that rewrites pages without inventing anything, and a pre-registered test on a real engine — which we report honestly, including that it failed.

Demo video

The problem

AI agents are the new front door — and today's optimizers need traffic

Buyers ask, agents answer

"Which cobot should I buy for welding?" now goes to ChatGPT, Claude or Perplexity. The answer names three brands.

They read the web for you

In our probes each engine read pages per question before choosing.

Low-traffic sites are left out

A/B testing and tools like Coframe learn from your own visitors. A cobot startup with a few hundred visits a month can't.

How it works

Learn a model of the engines, then optimize inside it

1 · PROBE

Ask real engines buyer questions, many times. Record pages read and brands named.

2 · SIMULATE

A cheap model answers from the same pages; validated against the real engines.

3 · EDIT

A Thompson-sampling bandit picks one of 7 edit types; an LLM applies it.

4 · CHECK

A claim checker rejects anything not supported by the original page.

5 · PROVE

Pre-registered head-to-head on the real engine, held-out questions.

Step 1 · What the engines recommend today

Two incumbents take most answers; engines disagree on the challengers

Share of answers naming the brand in its top 3 · 40 cobot buyer questions · Claude Opus 5.5, OpenAI gpt-5.5, Perplexity

Step 2 · Is the simulator any good?

Yes for overall rankings. No for single questions — and we say so.

Our first simulator (told to use "only these results") scored r = −0.05: it trusted vendors' own "best cobot" blogs. Letting it blend sources with its own knowledge — as real engines do — fixed it.

Step 5 · The pre-registered result FAIL

The real engine didn't confirm the gains — and showed us why

Noise: the same unchanged page, asked twice, scored 50% vs 38% (Franka) and 25% vs 38% (Elite).

So: simulated gains measured with 16 runs per edit sat inside that noise; the optimizer kept lucky edits.

Fix: many more simulator runs per edit, more questions, more final trials.

Per company

Explore the run

Numbers only — the companies' own pages and the rewritten versions are not published.

Keeping it honest

Every objection has a test

"But…"What we did
Your simulator isn't realityThe final test uses the real Claude Opus 5.5
Any rewrite would helpCompared against a generic AI rewrite and a length-matched placebo
You tuned on the test15 held-out questions never used for learning
You picked the winnersHypothesis, companies and analysis committed to git first (f7a0b9f, eee5a57, 0e88e8e, e70b303)
It's manipulationClaim checker; no hidden text; the same page for people and bots

Where it goes

The recommendation layer for the agentic internet

Always-on monitor

Weekly share of AI answers per engine; alerts when a model update shifts who wins.

Agent-facing pages

The same simulator for /agent pages, MCP endpoints and personal-agent protocols.

Shared calibration

Every connected site's real outcomes recalibrate the simulator for everyone.

Be found, not just chosen

Map which sources feed each answer — and where you're missing.

A trust standard

The no-invented-claims checker as a guardrail for self-optimizing sites.

Every market

Cobots today; B2B SaaS, dev tools and local services next.

Coframe optimizes for the visitors you have. AgentRank is building the way to get recommended by the agents that send them.