The question does not have a universal answer. An intranet with a knowledge graph is a living system that combines data, semantics, AI models, business rules and secure access. Therefore, the update frequency depends on the layer you are referring to and on the operational impact that each change has on the organization. Anyone looking for a single date is oversimplifying an ecosystem that, when properly built, learns and evolves with the company itself.
The data and relationship layer is the most dynamic. In a corporate intranet, the graph represents who knows what, which project a person participates in, what documents they produce and how teams connect. That data changes daily, even hourly. A monthly update would leave information obsolete and would make expert recommendations, skill profiles and searches lose accuracy. The recommended approach is to connect the graph to source systems through APIs, events or synchronization processes that keep nodes and edges up to date, with a minimum daily frequency and, in demanding environments, almost in real time.
The semantic layer, on the other hand, requires a more measured pace. We are talking about the graph schema: entity types, categories, metadata and inference rules. These elements define how information is interpreted and must not be modified without control. A biweekly or monthly review, with a governance committee that evaluates the impact on searches, permissions and automations, is usually enough. This allows knowledge to evolve without breaking the processes that depend on a stable structure.
The AI and agents layer connects with the previous one. An internal assistant that answers questions about policies or documents does not update at the same speed as the database. Language models need versioning, response evaluation, hallucination detection and gradual tuning. AI agents that execute tasks inside the intranet must be supervised with audit logs and human approval checkpoints. Updating these components is usually done in cycles, not every hour: when the model improves, it is validated with a test suite and deployed with a rollback strategy.
As for the application as a product, the cadence resembles that of any modern software. The interface, search functions and collaboration modules can be updated every two to four weeks through continuous integration. There is no need to wait for a major annual release. Small, frequent changes reduce risk and allow users to perceive constant improvements. Each release must include regression tests on graph queries and on the permissions associated with each profile.
Security updating deserves special treatment. An intranet with a knowledge graph concentrates sensitive organizational information and becomes an attractive target for attacks. Therefore, the infrastructure must receive security patches on a monthly basis, and urgent patches as soon as a critical vulnerability is detected. Access reviews, dependency analysis and periodic penetration testing are part of the lifecycle. Cybersecurity is not an initial step; it is a continuous process that coexists with every functional update.
Infrastructure also defines frequency. When deploying on AWS/Azure cloud services, the platform can take advantage of managed resources that update automatically. Q2BSTUDIO usually designs architectures with private environments and secure tunnels so that AI can interact with internal systems without exposing data. In this context, updating the graph also means reviewing the configuration of underlying services, network policies and backup mechanisms, not only changing content.
From a business perspective, it is advisable to appoint a graph owner. This person or team prioritizes what information must be updated first, validates data quality and makes decisions about new connections. Updating is not a technical problem but a knowledge governance issue. Without that role, layers end up being updated based on urgency rather than strategy. With BI/Power BI dashboards, for example, you can measure graph coverage, node age and the impact of updates on productivity.
Q2BSTUDIO addresses this challenge from custom software development. It does not impose a universal interval because every organization has different data sources, teams and risks. During the design phase, it defines which layers are updated automatically, which require human approval and which administration tool allows the business team to adjust rules, review logs and supervise AI agents without depending on the engineering department for every small change. Knowledge should be governed where it happens, not in a ticket.
Every relevant update should also review graph relationships: employee joiners and leavers, project changes, new skills, obsolete documents, revoked permissions. A badly updated node can generate wrong assistant answers or dangerous recommendations. Graph quality depends more on pruning than on accumulation. Therefore, each cycle must ask what information is no longer valid and should be removed or archived, not only what new information needs to be incorporated.
A practical guideline could be: operational data and relationships, daily; schema and semantic rules, every two weeks; AI models and evaluations, monthly; security and dependencies, monthly and after critical alerts; interface and automations, every two or four weeks. These frequencies are only a starting point. The architecture must allow changing cadence without rewriting the platform. Flexibility is more valuable than a rigid calendar.
In conclusion, an intranet with a knowledge graph is updated both in real time and in planned cycles, depending on the layer. Companies that get it right do not look for an annual maintenance date; they design a continuous update system with governance, metrics and security. The right technology makes it possible for corporate knowledge to remain alive, but the decision to keep it that way is strategic and must be made from day one.
