How can AI retain business knowledge when people or models change?

AI can reuse business knowledge after employees leave or models change only if that knowledge is captured outside an individual conversation, linked to its evidence and applicable context, and maintained under the business’s control. A retained explanation needs an owner, permissions, review status, and a way to correct or retire it.

What do Endpoint Memory and Harmonic Memory mean?

In OpenLock’s developing design, Endpoint Memory holds context associated with a permitted endpoint’s learning workflow. Harmonic Memory is intended to hold reviewed business knowledge that can be reused more widely where policy permits. The distinction concerns scope and authority; an employee’s conversation does not automatically become company-wide truth.

The Business Nervous System walkthrough shows two continuing exchanges: people and endpoint agents clarify local work; endpoint agents and the shared foundation exchange questions, proposed interpretations, and permitted knowledge. Uncertainty can return to the person authorized to resolve it.

An employee explains an inventory exception

Illustrative example: a planner explains that 80 seals are on a quality hold even though an inventory export includes them in on-hand stock. The explanation is initially a claim requiring review. A proposed record links the affected items, source snapshot, scope, and quality owner. After authorized review, the planning workflow can reuse the applicable rule and show its evidence.

If the hold ends, the rule must be corrected or retired. If the employee changes roles, the business reassigns stewardship and access. Historical evidence and current permission checks remain distinct; retaining a record does not mean every user may retrieve it.

What needs to stay with the knowledge?

• Meaning and scope: what the explanation means, where it applies, and exceptions.
• Evidence and provenance: source references, dates, and how the interpretation was reached.
• Authority: who proposed it, who may approve it, and whether it is awaiting review.
• Lifecycle: effective dates, review triggers, correction history, retention, and deletion rules.
• Access: who may read or reuse it, including through a combined answer.

Does model portability happen automatically?

No. Keeping approved knowledge in a customer-controlled foundation can reduce dependence on a model’s private conversation history. Changing models still requires testing the model interface, retrieval, permissions, evidence presentation, and answer quality. Export formats, transition support, deployment boundaries, and deletion responsibilities must be agreed and validated for the engagement.

OpenLock’s public material describes this intended architecture. It does not establish universal compatibility, a completed production platform, or guaranteed retention of every employee’s expertise. Some knowledge should expire, remain restricted, or never be captured.

How can a business begin?

Choose one recurring question and one accountable owner. Collect a normal example, a disputed definition, a changed rule, and an access-denied case. Agree what constitutes an accepted interpretation, then test correction and permitted reuse before expanding. See how this relates to a warehouse or RAG system and discuss a first workflow.