Building an Article Database
A living corpus of curated intelligence, tagged and queryable, that becomes the firm's long-term memory.
Articles, newsletters, research notes, and market commentary are the raw material of thematic conviction. This module covers how to build a structured article database — not a bookmarks folder, but a tagged, searchable, and AI-queryable corpus that feeds memo generation, partner updates, and expert programs.
Where this lives
The article database is the workhorse of the collaborative workspace layer of your stack. The home for it is Notion (or the equivalent workspace tool described in the Collaborative Workspace blueprint), sitting alongside — never inside — the canonical records in your CRM. Within the thematic stack, it is the durable surface that feeds the workflows in Module 05 · Weaponizing the Pipeline.
The worked example — the Article & Email Database
The clearest demonstration of how this module pays off is a single database we run called the Article & Email Database — an interface for directing what the team reads to wherever it belongs: email, SharePoint, Slack, or Attio. It turns the most common and most-wasted habit in a firm — reading something useful and meaning to share it — into structured, routed, queryable signal.
How content gets in. Every team member runs the Save to Notion Chrome extension. One click drops an article into the database with its title and URL captured automatically. No copy-paste, no “did you see this” lost in a DM. The reflex to share becomes a record of what the firm is paying attention to.
What it does. Each row is an article. A set of action fields decides where it goes, and AI handles the summarizing along the way:
| Field | What it does |
|---|---|
| Sends a thesis-tailored AI email summary of the article to yourself. | |
| Knowledge base | Sends the article to the firm's document store for ingestion by the RAG knowledge agent. On by default — everything is saved. |
| Knowledge base — company | Files the article under a specific company's “Relevant Articles” folder in the document store. |
| Team chat | Posts the link and a short AI summary, with context, to the market-research channel. |
| CRM | Creates or updates the company record with a Note containing the summary and the original content. |
| Context | Free text: anything you want the AI to know before it writes the summary or chat message — the angle, why it matters, who should care. |
Toggle an action and an automation routes the article to that destination, generating the summary where one is needed. One capture point, five destinations, the team's whole stack wired together behind a single Notion row.
Why it works
It is the separation principle and the interoperability thesis made concrete:
Canonical moves — article filed to a company's SharePoint folder, attached as a Note on the Attio company record, ingested into the knowledge base — land in the systems of record. Thinking-and-sharing moves — email digest, Slack summary with context — stay in the communication layer. Nothing blurs.
A single database reaches email, SharePoint, the knowledge base, Slack, and Attio. That is only possible because the workspace is built to connect outward.
Before the model summarizes, a person says what the article is for. The output reflects the firm's actual view instead of a generic abstract — capture that improves the corpus rather than padding it.
What someone read, and why, is exactly the high-signal context that usually evaporates. This database keeps it, in a place every downstream system can use.
Build notes
Schema: Title (text), URL (URL), Email (checkbox/select), SharePoint (checkbox, default on), SharePoint Company (text — the company name), Slack (checkbox/select), Attio (checkbox/select), Context (text). Ingestion: the Save to Notion extension, configured to write into this database with Title and URL mapped to the matching properties. Routing: an automation layer (Zapier, webhooks, or similar) watches the action fields and, on toggle, runs the AI summary step and delivers to the chosen destination(s).
What comes next
The article database captures the signal the team has already decided to look at. The next module — Module 07 · Pulling Down Trusted Sources Regularly — covers how to wire recurring inflows into the stack so the intelligence arrives before anyone has to go looking for it.