What is a company brain? And what is a GTM brain?

The short answer. A company brain is everything a company knows, kept current and usable by both people and AI agents. Day AI builds the go-to-market version: a context graph of every meeting, email and call, and an agent control plane where agents do the work and people approve it, running in place of your CRM or on top of Salesforce or HubSpot. A GTM brain is the go-to-market part of it: what the company knows about its customers, prospects and deals, built from the team's conversations, with agents that do the work on it, and Day AI is built to run it.

Say you run partnerships at a fintech company, and it's three o'clock on a Friday. Like every partner manager on the team, you cover 20 to 40 partners, and each one has a week of calls, Slack threads, email, HubSpot notes and Notion pages behind it. Your Day AI agent has already read all of it. Its partner history skill pulled the week's calls, Slack, email, HubSpot and Notion into the running record it keeps for every partner, and the refreshed records are waiting in Day AI for your approval, as they are every Friday. You skim the changes, approve them in Day AI, and every partner's record is current before the weekend. Once a month, those approved records feed the leadership pre-read, which has replaced the slide deck, and leadership likes it. Next month's pre-read starts from the records you just approved.

Day AI's view. The company brain is life after CRM: the conversations are the record, agents do the work, and people approve it. Day AI provides the context graph and agent control plane to quickly and securely upgrade your company brain. The usual answer, a smarter wiki or a bigger personal folder, goes wrong because it only knows what someone decided to write down, and it only works for one person. If you already keep a folder of notes with Claude Code on top, that was the right start.

Claims about other products are dated October 2026 and kept to what those products say about themselves. This is Day AI's guide.

---

Where "second brain" came from

The idea is older than AI. The Zettelkasten, a slip-box of linked notes best known from the sociologist Niklas Luhmann, was a second brain on paper. Tiago Forte's Building a Second Brain (2022) made the term popular, with the PARA method (projects, areas, resources, archives) for filing what you capture.

The AI version came in April 2026, when Andrej Karpathy posted his "LLM wiki" pattern and wrote it up as an idea file. You drop raw sources into one folder and never edit them. An LLM compiles and maintains "a directory of LLM-generated markdown files" from them, a wiki it keeps current, steered by a schema file such as CLAUDE.md or AGENTS.md. Plenty of RevOps leads and founders have built their own version, and Day AI takes that folder idea to the company: one brain built from the team's conversations, with an agent working for each person.

When companies say "company brain" today, most mean a shared knowledge base. In Slite's survey of 149 people, 53% described a continuously updated knowledge base and 12% a memory layer for agents, and Slite notes that YC's Summer 2026 Request for Startups names the company brain as a category.

---

Why a company needs one, and where a personal folder breaks

A business runs on what it knows about its customers: which buyers say yes, why deals stall, what customers were promised. Most of that was never written down. It was said on a call, in an email thread or in Slack. A personal brain serves one person. A company brain adds the whole team's conversations, who may see each one, and agents working for each person, and that's what Day AI builds. A personal folder breaks at company scale in five places:

• Many people's conversations. One person's folder holds one person's calls. The company's knowledge is spread across every rep, CSM and partner manager. In Day AI, each person connects email and calendar, a notetaker records meetings, and Gong, Granola and Slack channels come in, so the context graph holds the whole team's conversations.
• Privacy between teammates. A teammate must not read a thread you keep private, and a shared folder can't tell the difference. In Day AI, each person sets sharing rules for their own email (everything or nothing by default, with exceptions by domain, address or Gmail label), and every answer cites the call or message it came from.
• The CRM. The folder has a CRM export from last month; the team works in the live CRM. Day AI runs in place of your CRM, or on top of Salesforce or HubSpot, syncing through their MCP servers, which allow reads and writes when the write tools are turned on.
• Agents acting for each person. A Claude Code session runs as whoever opened it. Twenty reps need twenty agents, each seeing only what its person may see. In Day AI, every agent has a name, a job and skills, works for one person and inherits that person's permissions.
• Keeping it current without anyone filing notes. A folder is only as fresh as its last upload. In Day AI, the record fills itself from meetings, email and calls, and skills run on a schedule or when something happens, with no one filing anything.

In customer-facing work, a mistake lands on a customer: a private thread shown to the wrong rep, a follow-up quoting last quarter's pricing. The controls that keep agents safe are the same ones that let you hand them real work. This video builds a Claude Code and Obsidian second brain, then shows what changes once the team's calls go in.

---

What a GTM brain is made of, and which parts Day AI provides

A GTM brain is the company brain for go-to-market, and it is the one that matters most, because how a business makes money is the most important thing it knows. Its owner is usually RevOps, which already owns the pipeline and its definitions; What is a context graph? makes that case. Think of it as the three-layer GTM brain: the context graph, the agent control plane, and what your team builds. The first two are infrastructure; the third is yours.

1. A context graph. The people, companies, deals and conversations your team works with, linked, with every fact tied to its source and visible only to those allowed to see it. Day AI builds it from your team's meetings, email, calls and Slack.
2. An agent control plane. How agents work: each has a job and skills, works for a named person, and leaves a record of every run. You decide which work waits for a person, and every skill is versioned, so a change can be traced and undone. In Day AI, a skill only touches the connectors you attach to it, skills like Opportunity Automation start in learning mode, admins publish skills from a shared library, and the agent can bring back an earlier version of any skill.
3. What your team builds on top. Your skills, agent job descriptions, definitions (what counts as a qualified deal or a churn risk) and workflows. In Day AI, you write these as skills in plain language.

That split answers build or buy by category: buy the infrastructure, the first two layers, because it is difficult to build, barely differs between companies, and is where the security risk sits. Build the third. With Day AI, you buy the first two and keep building the third. The build-or-buy guide prices both sides.

---

Start with one folder: the GTM Brain repo

The company brain can still be one folder an agent works from. Day AI's own team publishes GTM Brain, "a Claude Code harness for running your Day AI workspace like a GTM operating system." It is one repo you open in Claude Code, with a CLAUDE.md of operating principles, planning documents, skills, and the Day AI MCP server (https://day.ai/api/mcp) already configured. Start with:

npx skills add day-ai/gtm-brain --global

1. Make it your team's repo. After installing, run /setup-gtm-brain, which creates your team's private copy.
2. Map your GTM before you have a workspace. Open the folder in Claude Code and run /start. With no Day AI workspace connected, it drives the map-your-gtm initiative: it reads the GTM docs, CRM export and org chart you drop into its discovery inbox, and anything you've already taught Claude, maps your tech stack, strategy, people, privacy posture and agent fleet, and scopes a first-win pilot.
3. Connect Day AI and implement. Once a workspace exists, an Owner or Admin authenticates the Day AI MCP server and runs /start again. From there the repo plans, audits and implements: it designs agents and skills for each person, and previews every change before it writes to the workspace.

---

How to start a GTM brain with Day AI

1. Point it at one number. Pipeline created, win rate, ramp time or rep earnings. In Day AI, write the number into each agent's job description, so every run starts from it. How to grade your AI agent setup turns this into a scorecard.
2. Connect the conversations. In Day AI, each rep connects email and calendar and turns on the notetaker, and an owner connects Gong, Granola or Slack. Agents work from your history once the first sync finishes, within hours.
3. Give each rep an agent with two jobs. In Day AI, every new agent comes with the Morning Briefing skill, a weekday briefing of priorities, meetings and recent action items. For follow-ups, ask the agent to "create a skill that drafts follow-up emails after meetings every evening", and the rep reviews each draft before it goes out.
4. Approve first, then widen. In Day AI, skills like Opportunity Automation start in learning mode: they report what they would have done and ask for feedback before they act. In chat, each connected tool is set to Always allow, Needs approval or Off, and those settings are chat defaults only. A skill uses the connectors you attach to it without asking, so attach only what that skill should touch.
5. Keep what you built. Your folder and Claude Code connect to Day AI over MCP, with OAuth, so your own Claude Code and your team's Day AI agents read the same customer memory.

The Vector 2026 talk at the top of this page is the long-form version of this argument.