The agent can reach the information.
- Search a drive
- Read a Slack channel
- Query a CRM
- Call an API
Company brain
For companies already building AI agents.
A company brain for every AI agent you build.
Company brain, defined
A governed context layer that gives every agent the same company knowledge, live operational state, memory, skills, and permissions.
Book an architecture call
30 minutes. Bring one real agent.
Connecting Claude, ChatGPT, or your own agents to company tools gives them access. A company brain tells every agent what is current, trusted, relevant, and allowed—without replacing the stack you already built.
01 · The context wall
Your agents are capable. They are also starting every shift on day one.
The first agent works. Then the second needs the same integrations, the same rules, and another version of the same company knowledge. The system gets more fragmented every time you add intelligence.
An agent without deep business context is a fancy API wrapper.
- 01
Every agent has its own prompt, connectors, and partial copy of company knowledge.
- 02
Two agents answer the same question differently because neither knows what is canonical.
- 03
The useful context lives in one person’s chat history instead of becoming company infrastructure.
- 04
Your team spends more time feeding agents context than letting them do the work.
02 · Access ≠ context
Connecting tools opens doors. It does not tell the agent what to trust.
The agent understands the business.
- Knows which fact is canonical
- Loads only relevant context
- Preserves decisions and state
- Acts through shared rules
03 · The shared layer
One company underneath every agent.
The model and interface can change. The company underneath them should not.
What is true right now.
Customers, projects, deals, approvals, owners, and live operational status—not another pile of documents.
What the company has learned.
Decisions, rationale, relationships, outcomes, and history that survive sessions and staff changes.
What this agent needs now.
The relevant slice of company knowledge, retrieved with provenance instead of dumped into every prompt.
How your company does the work.
Reusable procedures, standards, and definitions of done that every approved agent can follow.
What the agent may know and do.
Permissions, approval gates, audit trails, and safe actions designed around the risk of the work.
Same company context
Claude ChatGPT Codex Your agents
04 · How we build it
Start with one agent. Build the layer it should have had.
We follow a real context failure from architecture to production, then turn what works into shared infrastructure.
Map the context stack
We audit the agents you already have, where they get stuck, the systems they touch, and the knowledge they need to make good decisions.
Design the brain
We define the source of truth, memory model, permissions, retrieval, skills, interfaces, and guardrails for your actual operating environment.
Prove it on real work
We connect one working agent to the shared layer and deploy a real use case. Then we leave a roadmap for expanding it across teams and agents.
05 · Who this is for
For teams past the agent demo.
You do not need to believe agents are coming. You already have one—and now it needs to understand the company.
- 01
You already have at least one agent in development or production.
- 02
The agent needs context from several systems, not one clean database.
- 03
Freshness, permissions, provenance, or conflicting facts are becoming real problems.
- 04
You want the layer to work with your stack—not force every team into another chat product.
06 · Common questions
What teams ask before we start.
Why not just connect Claude or ChatGPT to all our tools?
Connections give an agent access. They do not decide which source is authoritative, resolve conflicting facts, preserve operational state, load only the relevant context, carry learning across agents, or govern what happens before an agent acts. A company brain does that work underneath whichever AI surface your team prefers.
Is this just an enterprise RAG system?
RAG retrieves passages. Useful, but incomplete. A working company brain also carries structured state, durable memory, reusable skills, permissions, approval gates, and feedback from what happened after an agent acted.
Do we need to replace our agent framework?
No. The point is to make company context portable. We design the shared layer to meet your agents through the interface that fits your stack, including MCP and APIs.
Are you selling a software subscription?
This starts as an architecture and implementation engagement. Refound audits the context problem, designs the layer, and deploys the first use case with your team. Ongoing operation and expansion can follow if the system proves valuable.
What should we have before calling?
At least one real agent use case and a concrete context failure: stale answers, repeated integration work, missing institutional knowledge, permission concerns, or an agent that cannot reliably tell what is true.
07 · Bring one agent
Show us where your agent loses the plot.
Bring one real use case and the systems it needs to understand. In 30 minutes, we can tell whether the missing piece is a company brain—and what the first useful version should look like.
Book an architecture call
Or write to [email protected]