Knowledge base and business map (ontology) of the Brain

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Conversa Labs

Last updated on Oct 13, 2026

Overview

The Account Brain has two pieces that work together:

The knowledge base (corpus) is the set of sources that feed the Brain: documents you upload, pages you paste by URL, and the objective/goal you write about your business. Each source is processed and split into indexed pieces (the "chunks"), which Maestro consults to answer with a reference to the origin.
The structure or business map (ontology) is the consultable result of that: a graph of entities (inboxes, teams, AI agents, labels, attributes, macros, channels) and the relations between them, built from the corpus plus the real data in your account. It's the "second brain" — the digital twin — Maestro uses to understand your operation.

In short: you feed the knowledge base and the Brain builds the business map from it. The richer the corpus, the better the answers, risks and insights.

Prerequisites

An account with Maestro and the Account Brain enabled (the account_brain capability). If you can't find the area, talk to an administrator.
Reading is for any account member: viewing the sources and the business map requires no special permission.
Adding or removing sources is administrator-only — the server re-checks the permission, and the interface hides the upload/delete controls for non-administrators.

Step by step

Feed the knowledge base

In the Knowledge base (corpus) tab, you have three ways to "feed the Brain":

Upload a document: drag the file into the upload area (or click to choose) and confirm. The file is stored as an attachment and the Brain fetches and extracts its content, turning it into indexed pieces.
Paste a URL: paste a page address and confirm. The Brain fetches the page and extracts the content, just like a document.
Write the account objective/goal: describe, in text, your business goal (there's also a voice dictation button to fill it by speaking). The objective is a fixed source: saving it again replaces the previous one instead of duplicating.

Manage sources

The list shows each source with its indexed-chunk count, plus a summary at the top with the total number of sources and chunks — the proof the Brain has been fed.
To remove a source, use the delete icon (administrators only) and confirm in the dialog. Removing a source deletes all of its chunks from the index.

View the business map (structure/ontology)

Open the Structure (ontology) tab to see the map built from the corpus and the account's real data. It is a tree: the account, four domains (Entry, Service, Automation and Context), the families of each domain (inboxes, channels, agent bots, teams, macros, labels, attribute scopes and custom attributes) and, inside them, the entities.
The bar above the map carries the account's indicators (entities, relations, inboxes with a bot, unlinked), the search and the controls. Click an indicator to highlight on the map exactly the entities that explain that number; Clear highlight or Esc goes back. The search lists its results by family and takes you to the entity you pick.
The chips right below are the legend: each family and each kind of relation. Clicking a chip hides or shows that family or that kind; double-clicking a family leaves only that one.
Each domain shows its real coverage (for example, "13 of 24 inboxes with a bot") and each family shows how it is split (inboxes by channel, attributes by scope, how many are linked). Accounts with many entities open in the overview; click a family to open it or use Expand all. Large families show one part at a time, with Show the rest.
The lines with an arrow are the relations the Brain mapped (for example, "uses bot" or "assigns to team"). Between closed families they are grouped, with the kind and the count; once an entity is selected, they appear one by one. The thin lines with no arrow only show what each part belongs to.
Click an entity to open the detail panel with its path to the account, what the Brain knows about it (channel, provider, type, key, scope), its relations by kind and, when present, the sources. Open in product takes you to the screen where it is configured. An entity with no mapped link stays on the map and the panel explains why: either that kind of link has not been mapped for this account yet (use Map now), or it has and nothing points at it.
Under View you choose the layout (Hierarchy or Neural), the density of the cards (compact, comfortable or detailed), how relations are drawn (bundles or strands, labels, animation, visible kinds) and whether the focus isolates the neighbourhood of the selection. The choices are kept in this browser; Restore defaults undoes them all.
The bar, the legend, the minimap and the zoom can be visible, compact, minimized or hidden. Clean mode (H key) leaves just the map, with Show controls always in sight; full screen uses the whole window and Esc goes back.

Settings & options

Embeddings provider (administrators): at the top of the knowledge base you choose which provider indexes and searches the account's knowledge (installation default, OpenAI or Cohere). Switching re-indexes every source automatically — wait for processing to finish before judging answer quality.
Three input modes: document, URL, or objective text — pick whatever fits each piece of content.
Objective as a fixed source: re-saving the objective reindexes (replaces); it never piles up duplicates.
Per-source chunk count: shows how much content was actually extracted and indexed.
Fail-safe behavior (cache-first): the business map is cached, so the screen renders even if the engine is momentarily offline — instead of showing an error.
Teaching empty-state: when there's no knowledge yet, the screen invites you to feed the base rather than showing a dead screen.

Use cases

Centralize manuals, policies, catalogs and FAQs so Maestro answers consistently.
Record the business objective to guide the copilot's proposals and actions.
Visualize how inboxes, teams, agents and attributes connect — useful to review your account setup.
Build a solid base that improves the risk analysis and insights generated per department.

Tips, limits & best practices

The richer and cleaner the corpus, the better: up-to-date, well-written documents produce more accurate risks, insights and analyses.
A clear objective improves the proposals from the copilot and the departments.
Avoid uploading outdated content — old information yields old suggestions.
The business map populates over time: feed sources and wait for processing.
Indexing uses embeddings with automatic provider fallback (e.g. OpenAI → Cohere). Connect a Cohere key under Integrations so the knowledge base keeps indexing even when the primary provider runs out of credits.

Troubleshooting

Source with 0 chunks: the extraction found no content — the format may be unsupported, the file may be empty/protected, or the URL may be unreachable. Try another format or re-upload.
Business map (ontology) is empty: the map does NOT come from the sources you upload — it is built from your account's real structure (inboxes, teams, bots, labels, attributes, macros). It is generated when the account is created and refreshed once a day after that. On an account created right after the daily refresh, the map stays empty until the next one. Use Map now, on the empty screen itself, to build it immediately — it takes a few seconds.
"Could not map this account": Maestro could not read your account's structure. This is almost always the account access token Maestro uses; ask an installation administrator to review it and map again.
Action blocked: uploading or removing sources is administrators only. If the controls don't appear (or the action is refused), confirm your role with an administrator.
"Indexing failed" / embeddings unavailable: no embedding provider has a valid key with credits. Check the primary provider's credits (e.g. OpenAI) or connect a Cohere key under Integrations and try again.
Incompatible embedding model while saving a Robot: each installation stores vectors at one fixed width. Pick a model with the same dimension shown on screen — on a 1536 installation, use text-embedding-3-small instead of text-embedding-3-large (3072). After changing a model that has already been used, reindex the knowledge base so old and new vectors are not mixed.