Combot knowledge
Frameworks, playbooks, references.
The Combot point-of-view in long form. Every framework that drives the dashboard, every playbook a practitioner can pick up and run, and every reference an engineer needs to plug us in. Start with the framework. Operate from the playbooks. Engineer with the references.
01 — The framework
How we think about AI visibility.
The 7 layers of AI visibility
Seven measurable stages from Trained-on to Acted-on. The Combot funnel, mapped to the three Knowledge Modes and aligned with how the industry measures generative visibility.
Tools for Modes: per-vendor LLM tool implementation
The technical reference companion to Knowledge Modes — what Claude, GPT, Gemini, Perplexity, and Grok actually ship, with history.
AI Knowledge Modes: Memory, Search, Fetch
Every AI answer about your brand is composed from three modes. They fail differently. They reward different work.
AI Share of Recommendation: a new north-star metric
Why "are we mentioned in ChatGPT?" is the wrong question, and what to track instead.
02 — The GEO Matrix series
Six posts. One per cell. Operationally complete.
Generative Engine Optimisation has three knowledge modes (Memory · Search · Fetch) and two operational verbs (Optimise · Measure). The 2026-05 series gives each cell a dedicated post. Read the Knowledge Modes foundation first.
Pair-linked posts: each Optimise links to its Measure counterpart and vice versa. Series footer references PrerenderProxy for the Fetch column.
03 — Optimisation playbooks
How to actually move the metric.
Server logs for the AI era
The log file is the AI visibility seismograph. Brave-bot crawl frequency predicts Claude Search Mode visibility.
Lean Render: AI-bot rendering without the JavaScript trap
Dynamic rendering died for Googlebot. AI bots brought the problem back — Lean Render is what comes next.
From fetchability to trust: the technical SEO of language models
AI bots crawl differently. Most don't execute JavaScript. Most never reach your category pages.
Source mapping: tracing an LLM's answer back to its roots
The citation is not the source. The source is whoever wrote the page the model paraphrased from.
Fact-checking the machines: building an automated accuracy center
Hallucination isn't a bug to dunk on — it's a KPI to track, alert on, and remediate.
More coming: AI-bot prerendering · server-log analysis · the channel-mix playbook · entity-graph hygiene.
04 — Measurement
How we measure it inside Combot.
The dashboard surfaces are documented in Combot's internal specs. Public summaries land here as each ships into the elisa pilot.
05 — Integration reference