In 2026, companies in Las Palmas de Gran Canaria face a challenge that cannot be solved with more tools: making sense of the growing volume of information they generate every day. Data lives in management systems, shared documents, emails and databases. An intranet with knowledge graph turns that chaos into a searchable network where people, projects, customers and documents connect naturally.
The traditional intranet was a useful step at the time, but today it falls short. A portal of news and links does not help people decide. A knowledge graph, on the other hand, models business knowledge: it represents relevant entities and, above all, the relationships between them. Thus, an employee not only finds a file, but understands which project it belongs to, which supplier is involved and what decision should be made.
In a mid-sized company in Las Palmas, the starting point was similar to that of many organizations: processes that depended on constant questions, information scattered between the ERP and CRM, and managers who received late and unreliable reports. The decision was to bet on a custom-built solution, with a modern architecture and artificial intelligence integrated from the beginning.
Q2BSTUDIO is a software development and technology company specialized in this type of transformation. Its proposal combines custom software, AI, automation and AWS/Azure cloud to create an intranet with knowledge graph that adapts to the client's real processes. It is not about implementing a closed product, but about building a platform that evolves with the organization.
The first step was to design the knowledge map. For several weeks, the Q2BSTUDIO team worked with the people responsible for each area to identify information sources, approval flows and exceptions known only by the most experienced staff. That analysis made it possible to define the entities of the graph: customers, projects, orders, documents, contracts, employees and suppliers, together with the relationships that exist between them.
Once the model was defined, the integration layer was built. The platform connected to existing systems through APIs and synchronization services, so the graph stays updated without manual intervention. Data is born in the source system, normalized and incorporated into the intranet with its context. This is one of the key points of the project: the client was not asked to change their favorite tools, but rather to make those tools work together in the same environment.
Artificial intelligence provides the conversational layer. AI agents query the graph and return meaningful answers, citing sources and explaining their reasoning. An employee can ask: 'What is the status of the project for customer X?' and the agent not only locates the project, but summarizes open issues, pending milestones and missing documents. When an action requires approval, the system stops and notifies the responsible person.
Process automation was another important pillar. Repetitive tasks that were previously handled with emails and spreadsheets became controlled flows: file creation, expiration alerts, assignment of responsible people and status updates. Each flow records what happens, which facilitates auditing and continuous improvement. The goal is not to remove people, but to free time for tasks that create value.
Security was treated as a structural part of the system, not as a final addition. The intranet with knowledge graph was deployed on AWS/Azure cloud with corporate cybersecurity standards: centralized authentication, role-based permissions, access logs and data encryption. GDPR compliance guided every design decision, from log retention to consent management.
The reporting layer also benefited from the graph. With connected information, the indicators that were previously collected manually are now calculated automatically. The management team uses Power BI to monitor the evolution of service times, number of automated tasks, usage per department and workload distribution. That visibility makes it possible to make decisions with data, not intuition.
Implementation was organized in phases to reduce risk. First, a quick diagnosis was carried out with the baseline KPIs. Then a minimum viable product was built, focused on the most critical processes. Next, it was extended to a pilot group, user feedback was collected and the AI models were adjusted. Finally, use was extended across the organization and local administrators were trained.
A part of the success that does not appear in the metrics was change management. People trust a system when they understand what it does and when a human intervenes. Therefore, time was dedicated to explaining the new flows, answering questions and demonstrating that the intranet does not replace professional judgment, but supports it with complete and up-to-date information.
The results of the success case in Las Palmas were measured with real data before and after the implementation. The manual workload in administrative tasks, the response speed to customers and the accuracy of internal processes experienced a notable improvement. The company recovered the investment in less than a year and, more importantly, gained capacity to take on new challenges without increasing headcount.
Q2BSTUDIO differs from a classic consultancy because it executes. Beyond analyzing and recommending, its team designs the technical solution, writes the code, integrates systems and supports operations. The areas of custom software, AI, cybersecurity, AWS/Azure cloud and BI/Power BI work together, so the client does not have to coordinate several providers with different interests.
The conclusion for a company in Las Palmas de Gran Canaria is clear: if the current intranet only serves to publish announcements, it is missing the opportunity to turn knowledge into a competitive advantage. A well-modeled knowledge graph, with AI and automation, is the foundation for teams to make better decisions and respond with agility.
Q2BSTUDIO supports mid-sized organizations in this process with a practical and measurable approach. An initial conversation about objectives and available systems is usually enough to know whether an intranet with knowledge graph is the right step. In 2026, the difference is not having more data, but knowing how to connect the data that already exists.
