Two ideas,validated in the open.

Everything a company knows. One graph.

What a company knows — roles, processes, metrics, rules, decisions — is written down, but no two people would look in the same place. So you ask a person instead; that is the tax. CompanyGraph is the structure that knowledge takes: a graph of Markdown, versioned like code, readable for people, reliable for AI.

1. 01

   Core

   Core types, one schema per type — the vocabulary two companies that never met converged on, and the one any company can be described in. Published and versioned, every release tagged.

   vocabulary published
   Open source
2. 02

   The packs

   Beside core, vocabulary only some kinds of company need at all, each taken by the company that needs it:

   • Software, for a company that builds software: its bounded contexts and their aggregates, in domain-driven design’s terms. Watch the talk
   • Organization, for a company of more than one person: its units, its teams and who answers to whom. Watch the talk
   • Landscape, for a company that runs systems: the systems and the data that passes between them, aligned to ArchiMate® 4. Watch the talk
   • Requirements, for a company that says what its products must do before it builds them: what someone wants from a product and what the product is held to. Watch the talk

   packs published
   Open source
3. 03

   The graph of Markdown

   One page per thing, its title is its name, every reference by name — nothing points at nothing, nothing gets quietly lost.

   conventions published
   Open source
4. 04

   The command and the plugin

   A command ships inside the meta-model. Run with nothing after it, it opens a menu that does all of it: init creates an instance that passes the checks on its first day, check checks it mechanically, upgrade keeps it current with core, obsidian installs the plugin in a vault, and adopt gives a repository that is not an instance the same form check and workflow. judge, run on its own, asks a decision model whether each page keeps its schema’s writing rules: advisory, and only after the owner agrees to what it sends. init also installs the agent skills that validate an instance, judge its writing, export it and produce its surfaces — and that build one, from a person’s documents or from a company’s web address. An Obsidian plugin ships beside it: the model’s checks run while a page is edited, and what a schema declares completes as it is typed. The plugin already writes a new page from its schema; the command does not yet.

   CLI and plugin shipped
   Open source
5. 05

   The MCP server

   An MCP server that ships: an AI agent connects and asks the model directly — which types a company declares, what one entity says, what evidence a claim rests on — read-only, serving any instance, running on the reference instance, on CompanyGraph’s own and on GuestGraph’s, and listed in the MCP Registry. It also draws part of the model as a diagram: the concepts, a process, an entity’s connections, the meta-model itself and, in a model that takes the software pack, a bounded context’s map, its aggregates, their flow and their lifecycle, and, in one that takes the landscape pack, a system in its landscape and what it holds.

   server shipped
   Open source
6. 06

   The chat

   A chat ships over the server: a visitor who will not connect an agent asks on the page in their own words, and the answer names the entity each claim rests on, sets a grouping out as a table and shows a structure as the diagram the server drew, and it says first that an AI answers there, not a person, as the AI Act asks. The command, the plugin, the server and the chat are open source — not the paid half of a free thing.

   chat shipped
   Open source
7. 07

   The talks, the example and real instances

   Deck, PDF and audio in German and English, plus one worked example — and real instances rather than invented ones, each public and checked against the conventions: mine, drawn as a graph here, CompanyGraph’s own and GuestGraph’s.

   real instances public
   Open source
8. 08

   Consulting

   Building the model with you. One meter: billed per day, time and material — stop any time, everything stays yours.

   model defined
   Commercial

A model meant for people and AI at once must not cost more the more of them read it.

intro talk · 7 min core and packs talk · 7 min the concept: mental model talk the model · drawn worked example the CLI billing model github.com/companygraph

Five strangers. One guest.

A guest books, checks in, eats, joins the wifi and leaves a review — and every system in the hotel stores someone different. GuestGraph resolves those strangers into one profile, and can always tell you why. A wrong merge is a data protection incident, so every decision is explainable and reversible.

1. 01

   The engine

   Identity resolution — deterministic and probabilistic, layered by confidence, with a human review queue.

   built
   Open source
2. 02

   The guest graph & API

   Immutable source records, a derived profile that can always be recomputed, everything reachable over REST.

   built
   Open source
3. 03

   Explain, undo, audit

   Every merge answers “why?” and can be split again — an undo writes a permanent do-not-merge rule.

   built
   Open source
4. 04

   Connectors

   Apaleo, a real PMS: reservations and bookings, by webhook and by full sync. Every other system — POS, wifi, booking engines — is still ahead.

   Apaleo shipped
   Open source
5. 05

   The hosted service

   Hosting, console, MCP for AI agents on the hotel’s own guest graph. One meter: billed per arrival — a number already on the hotel’s own dashboard. Planned, not open yet.

   model defined
   Commercial

Open core, because a black box that merges guests is not something I would deploy either.

intro talk · 6 min billing model github.com/guestgraph