Company Brain With Supermemory: A Practical Evaluation

You can evaluate Supermemory as the memory engine behind a company knowledge workflow, but its former hosted Company Brain is discontinued. Start by choosing between building around the current engine and operating the separately published open-source application, then test source quality, access, and corrections before widening use.

The other question is where approved work lives. fenbs keeps projects, tasks, Decisions and rules, and checkpoints shared across supported AI apps. That is useful when your team needs a maintained answer to “what happens next?” alongside a system that retrieves organizational information.

How do you begin a company-brain evaluation?

1. Define one job, such as finding the approved onboarding policy or explaining the background to an active project. Name the people who will use the answer.
2. Select a small set of permitted documents with known owners. Include a current policy, a replaced version, and a question the collection cannot answer.
3. Choose your operating route: integrate the current memory engine into an application or evaluate the separate open-source Company Brain code with technical ownership.
4. Test answers, source traceability, access boundaries, corrections, and removal. Keep a record of what passed and what remains uncertain.
5. Assign ongoing owners for sources and approvals. Expand the pilot only after someone can maintain both the knowledge and the resulting work.

A narrow pilot makes errors explainable. If an assistant retrieves the wrong policy from ten familiar documents, you can inspect the cause. If you connect every source immediately, it becomes harder to tell whether a wrong answer came from missing information, stale information, or the application’s handling of the result.

Which Supermemory offering are you evaluating?

Supermemory’s product update↗ (opens in a new tab) says the hosted Company Brain and Nova were discontinued, while its memory API, MCP, and plugins continued. MCP is a standard way for an AI app to use an outside tool. Avoid following an old hosted-product setup guide without checking which offering it describes.

There is also an official Company Brain repository↗ (opens in a new tab). Its README describes an open-source application that runs on your own Cloudflare account. That is an operating responsibility rather than the old hosted signup path; the separate company brain on GitHub guide covers that route.

The current Supermemory overview↗ (opens in a new tab) describes memory, retrieval, and profiles supplied through an API, a software connection your application calls. For a general introduction, read what Supermemory is. Here, the practical question is whether your organization can build and maintain the workflow it needs around those capabilities.

What should your first source collection contain?

Use material whose meaning and ownership are clear. A policy with an owner and an effective date is easier to evaluate than an unreviewed transcript of people considering several options. Both can be useful sources, but they should not have equal authority when the assistant answers a question about what employees must do.

Record expected answers before running the pilot. Otherwise it is easy to accept whatever the system produces and adjust your expectations afterward. Include ordinary wording an employee might use, an ambiguous project name, and a request that crosses two departments. Check the evidence behind each answer, including its qualifications.

Keep the project’s source choices and known gaps with the work. In fenbs, an AI context note can explain which reference is authoritative while a task tracks the review still needed. You do not have to turn every source into a permanent rule.

How do you check organizational access?

Draw the access boundary before choosing a connector. Decide which information is shared company knowledge, which belongs to a project, and which should never enter this pilot. A retrieval workflow must apply the requesting person’s allowed access; a label that says “private” is not enough evidence that every reading path enforces it.

Supermemory documents multi-tenancy↗ (opens in a new tab), meaning separation among users, teams, or other groups. Have the technical owner review its current isolation model and map it to your application’s permissions. Then test the application with separate identities. Test a denied request as deliberately as a successful one.

For example, a hypothetical operations team could use harmless sample documents marked for different groups. Ask each identity the same question and inspect what reaches the final answer. Also check what happens after access is withdrawn. Do this before introducing sensitive organizational material; the goal is a repeatable test, not an assurance based on a product label.

How do you keep retrieved knowledge from becoming false approval?

An answer can accurately report that someone proposed a change while still misleading the reader about whether it was accepted. Keep source statements, inferred conclusions, and approved choices distinct. Require a named person to confirm an instruction before it becomes the team’s current rule.

Use a correction test in your pilot. Replace a sample instruction, update the authoritative source, and ask both the original question and a differently phrased version. Check that the application presents the new answer and explains the old material appropriately. Also test removal so an obsolete source does not quietly remain a dependency.

The general AI brain guide explains why durable context matters. For company work, someone must also maintain the task state: who owns the next step, what was checked, and what is waiting on approval. Retrieval provides evidence for that work; your team still decides how to proceed.

How to do this with fenbs?

1. Create a free account and add a project for the company-knowledge pilot. Use a team kanban board when colleagues need to share the work.
2. Add tasks for source review, access testing, correction testing, and unresolved questions. Put the implementation approach and expected checks in each task’s plan.
3. Record the organization’s approved source and access rules in Decisions and rules. Name the person who decided; keep choices awaiting that person open.
4. Connect a supported assistant from Integrations. Have it read the project context before work and save task and project progress before stopping.

fenbs does not have a native Supermemory integration and does not provide a retrieval backend for your application. Use a memory engine when you need to build retrieval; use fenbs when your lasting problem is keeping shared work, approvals, and handoffs current. You can evaluate each role separately in the memory tools hub.

Finish the pilot with a decision your team can explain: the job it solved, the tests it passed, the risks still open, and who will maintain it. A company brain earns broader access through a working process for keeping information correct and appropriately shared.