What is an enterprise AI memory system?+
An enterprise AI memory system is the infrastructure that lets AI agents preserve, retrieve, and apply business context across tasks and sessions. It can include RAG, vector databases, knowledge graphs, structured summaries, workflow state, user preferences, customer history, and evaluation feedback.
How is AI memory different from a context window?+
A context window is temporary — the information the model sees during a single call or session. AI memory is persistent infrastructure that stores and retrieves useful information across sessions, workflows, users, customers, and business processes.
How is this different from RAG?+
RAG retrieves relevant information from external sources and injects it into the model prompt. AI memory is broader. It includes RAG, but also stores session history, prior decisions, user preferences, workflow state, corrections, and business outcomes.
Do we need a vector database?+
Often, yes, but not always. Vector databases are useful for semantic retrieval over unstructured text, but many enterprise memory systems also need keyword search, metadata filtering, relational databases, graph databases, and structured summaries.
Is long context enough?+
No. Long context can help with synthesis over a known set of documents, but it does not replace retrieval, memory governance, access control, or evaluation. Large contexts also increase cost and make it harder for the model to distinguish what matters.
How do you prevent AI memory from storing bad information?+
We use memory write rules, confidence thresholds, human approval, source attribution, retention policies, deduplication, and evaluation. Sensitive or uncertain information should not automatically become permanent memory.
How do you evaluate whether memory is working?+
We test retrieval quality, memory write quality, context assembly, task completion, permission compliance, and improvement over time. A good evaluation set includes realistic business questions, expected sources, expected outputs, and failure labels.
What systems can be connected?+
Common systems include Salesforce, HubSpot, Jira, Linear, ServiceNow, Slack, Teams, Google Workspace, Microsoft 365, Confluence, SharePoint, GitHub, internal databases, document repositories, and data warehouses.
How long does implementation take?+
A context and memory assessment takes 2–4 weeks. A prototype can be built in 4–6 weeks. A production pilot typically takes 8–12 weeks depending on data access, permissions, integrations, and governance requirements.
Who owns AI memory inside the organization?+
Ownership is usually shared. IT, data, security, and AI platform teams own the infrastructure and controls. Business teams own workflow knowledge and quality feedback. Compliance and legal define retention, privacy, and audit requirements.
Should we build or buy?+
Most enterprises should use a hybrid approach. Buy commodity infrastructure — connectors, model access, vector stores, observability, and security tooling. Build the differentiated business memory layer around your workflows, customers, policies, and operating model.
