How does the ReBo method work?
The ReBo method is an ongoing four-stage pipeline that turns the content a financial firm already produces into citations by AI models: 1) content comes in in whatever format it lives in today, 2) we extract and structure it into semantic blocks, 3) we publish it as AI-friendly pages, and 4) the models start citing it — ChatGPT, Perplexity, Gemini, Claude, and the rest — with monthly tracking.
STAGE 01What does the firm need to send?
Only what already exists: investor letters, quarterly reports, investment theses, market commentary — PDF, Word, or anything else. No rework for the investment or IR team. The thinking is already done. The problem is the format it's trapped in.
STAGE 02How is the content structured?
We break each document into semantic blocks: a heading hierarchy (H1/H2/H3), a 40–80 word executive summary, numbered theses, an FAQ built around the questions allocators actually ask, and structured data in JSON-LD. The denser the facts, the more citable the content — so every block keeps its figures, dates, and named entities next to the source they came from.
STAGE 03What does AI-friendly publishing mean?
The blocks become static HTML pages — every word in the initial HTML, nothing waiting on JavaScript — with Article and FAQPage schema, a canonical tag, visible publish and update dates, and three to five internal links per page. That's the format AI crawlers can actually read. The acceptance test is "view source": if the text isn't there, the page isn't done. More on why AI models can't read PDFs.
STAGE 04How does citation happen and how is it measured?
Once the pages are indexed (Google, Bing, and IndexNow; ChatGPT pulls from the Bing index), AI models start finding and citing the firm. Every month, ReBo runs a defined set of target questions through ChatGPT, Perplexity, Gemini, and Claude and records which answers name the firm. Those results drive the roadmap: new pages for the questions that aren't earning citations yet.
What does each stage deliver?
| STAGE | DELIVERABLE | CADENCE |
|---|---|---|
| 01 · Intake | Diagnostic plus an inventory of existing content | At onboarding, then with each new piece |
| 02 · Structuring | Semantic blocks + validated JSON-LD | Per document |
| 03 · Publishing | HTML pages, articles, FAQ, glossary, social cutdowns | Monthly |
| 04 · Citation | Citation rate and metrics report across AI models | Monthly |
Source: ReBo method, 2026.
The pipeline exists to move a firm from "Level 1 — Invisible to AI" to "Level 4 — AI Reference" in the AI Visibility framework. To start with the diagnostic, book a call.
Frequently asked questions about the method
Does my team have to produce new content?
No. The method starts from the content your firm already produces: investor letters, reports, investment theses. ReBo handles extraction, structuring, and publishing. Your team just signs off.
Does the published content replace the PDF?
No — the two run side by side. You keep distributing the PDF to clients. The structured HTML version is there for AI models and search engines, with an executive summary, an FAQ, and structured data.
How long until AI models start citing us?
Crawlers like GPTBot and PerplexityBot revisit active sites within days or weeks. Steady citations depend on volume and freshness, which is why the pipeline runs continuously with monthly tracking.
How are results measured?
By citation rate: the share of target questions where the firm shows up in answers from ChatGPT, Perplexity, Gemini, and Claude, measured month by month, plus referral traffic from those platforms.