The corporate intranet with AI search has become a strategic asset for companies looking to remove information silos and accelerate decision-making. In 2026, a real project led by Q2BSTUDIO in Granada showed that installing a search tool is not enough: what companies need is a knowledge platform combining custom software, enterprise AI, process automation, and a strong security approach. This article describes the case, the technical solution, and the measurable results achieved in less than five months.
The company in the case study, a Granada-based services firm with around forty employees, lived with a very common reality: information was scattered across shared folders, email, management systems, and local files. Each department had its own convention for naming document files, there was no single source of truth, and new employees needed weeks to become productive. Time spent searching for data, checking versions, and reviewing internal processes consumed valuable resources and caused errors that spread across the company.
The management team decided to solve the problem with a corporate intranet with AI search, but they did not want an off-the-shelf product. They needed a system able to understand natural language, extract precise answers from internal documents, and connect with the tools the company already used. That is when Q2BSTUDIO entered the project, applying a methodology based on discovery, architecture, integration, and continuous optimization. The goal was not to demonstrate technology but to solve a specific business problem.
During the discovery phase, the team analyzed workflows, friction points, access levels, and data retention policies. With that information, Q2BSTUDIO designed an intranet platform based on custom software development, with a semantic search engine able to index documents, emails, meeting minutes, internal policies, and project records. The solution did not replace existing systems: it turned them into connected information sources.
The result was an internal assistant that, instead of showing a list of files, provides a written synthesis, citing sources and showing context. If an employee asks, 'what is the procedure for approving an invoice?', the system locates manuals, related emails, and previous cases, then returns an executive summary with exact steps. This approach dramatically reduced search time and eliminated many repetitive questions to managers.
From a technical perspective, the architecture combined cloud services on Azure, language models, process orchestration with AI agents, and an API integration layer. The infrastructure was designed with secure VPN connections and private endpoints so AI models could access data without exposing it to the public internet. The system also included hallucination guardrails and human validation for critical decisions. Q2BSTUDIO's experience in enterprise artificial intelligence made it possible to balance general-purpose models with tuning on company documentation. The same architecture can be deployed on AWS.
Security was a pillar from the start. The authentication system was integrated with the company's active directory, and role-based access control was applied so each person could only see the information they were allowed to see. The platform recorded audit logs for all queries and administrative actions. This design helped the company comply with GDPR and reassured the data protection officer, who was able to define retention periods, special permissions, and incident protocols. Cybersecurity was not a later phase of the project but a cross-cutting requirement.
One of the most valued elements was the Power BI dashboard, integrated into the intranet itself. Managers can see in real time which areas generate the most queries, which documents are outdated, which workflows have the most errors, and which teams use the system most intensively. This visibility turned the intranet into a source of continuous improvement. In addition, AI agents were defined to automate administrative tasks: they classify requests, update records, alert about deadlines, and escalate exceptions.
Results were measured with KPIs defined before writing the first line of code. In fourteen weeks, the average time to locate a document fell by 58%, duplicated internal queries were reduced by 41%, and the onboarding process for new employees went from ten working days to four. Accuracy in resolving standardized requests reached 94%, compared with an initial 76%. The estimated operational savings during the first six months allowed the company to recover the investment earlier than planned, and middle managers freed up several hours of administrative work every week.
Implementation was carried out in phases to reduce the impact on daily operations. In the first stage, a pilot group of fifteen users worked with the search tool and reported problems, nuances, and suggestions. Later, access was expanded to the entire workforce and the remaining systems were connected. This incremental approach made it possible to adjust the models, fine-tune permissions, and ensure that the assistant's answers were relevant before mass adoption.
The main lesson is that an intranet with AI is not purchased: it is built around the data, people, and processes of each organization. The quality of the result depends more on information governance and good integration than on choosing the latest model. It is also clear that the internal team needs training and autonomy to maintain the system. For this reason, Q2BSTUDIO included a management portal that allows product owners to update sources, review conversations, and configure responses without depending on engineering.
A corporate intranet with AI search is a business decision, not just a technological one. The Granada case confirms that a medium-sized company can obtain measurable benefits in less than a quarter if it combines the right approach, the right technology, and a team with real experience in AI projects. Q2BSTUDIO provides the technical capability, functional vision, and ongoing support needed for the organization not only to launch a new tool but also to learn how to manage and improve it continuously.
