A corporate intranet with AI has become a strategic asset for companies that want to improve internal communication, speed up employee onboarding and reduce time lost searching for information. But the promise of AI is not fulfilled simply by installing a chat interface. Behind a good semantic search there is a data architecture, a permission model and an experience design that must be evaluated rigorously.
Evaluating providers does not start with the budget, but with the context. A company with 200 employees and one with 5,000 have different needs: one will require fast implementation; the other, governance and scaling. The ideal provider should ask questions about how teams work, which tools they use, where data is stored and which decisions the company wants to automate. If the provider does not ask those questions, it is probably selling a standard solution that will need expensive adaptations later.
From that diagnosis, a plan with clear KPIs can be built: reduction of search time, self-service rate, lower internal ticket volume, better onboarding experience. These indicators should be agreed before signing and reviewed in each iteration. Without metrics, the intranet becomes an endless project that no one knows how to measure.
One of the most important decisions is choosing between a generic platform and custom software. Closed platforms offer initial speed, but they may fall short for complex integrations, workflow personalization or security requirements. Custom software, on the other hand, lets you design the intranet around real processes, with simpler interfaces and evolutionary maintenance. Q2BSTUDIO always recommends analyzing the total cost and the ability to change before choosing an option.
Integration with the technology ecosystem is another pillar. An intranet with AI must connect with the company's identity provider (Azure AD, Google Workspace, Okta), the ERP, the CRM, the document manager and collaboration tools. The provider needs to demonstrate experience integrating REST APIs, webhooks, databases and message queues. It must also explain how permissions are synchronized so that AI does not leak information.
In the AI area, semantic search and AI agents make the difference. A well-built system must understand complex questions, combine several data sources, summarize answers and cite provenance. Quality is achieved with an orchestration layer that decides which tool or model to use depending on the query. The provider must explain how the model is trained or tuned, how drift is controlled and how user feedback is incorporated to improve results.
Cybersecurity is not an optional module. You should ask about data encryption, authentication, access control, protection against prompt injection and logging of actions. It is also important to know whether the provider performs pentesting or code reviews. In regulated sectors (healthcare, insurance, public administration), the intranet must comply with specific regulations and allow each query to be traced.
Infrastructure affects privacy and cost. AWS/Azure cloud environments make it possible to deploy AI services with high availability and scalability, but it is necessary to decide which data can leave the corporate network. An experienced provider can configure private clouds, VPNs, private endpoints and retention policies. The cloud is not a single destination: it is a tool that must adapt to each company's data strategy.
Measuring with BI/Power BI makes it possible to know whether the intranet adds value. It is not enough to have usage reports: information must be crossed with business KPIs, such as incident resolution time, productivity per department or employee satisfaction. A good dashboard shows what employees are searching for, what answers they receive and which processes are being automated.
Governance is another critical aspect. The intranet must define who creates content, who reviews it, who has publishing permissions and who supervises AI. This becomes especially important when search includes agents that perform actions. Human approval flows must be established for sensitive operations and logs must be reviewed periodically.
The delivery methodology must be agile and transparent. We recommend being wary of 12-month contracts with no partial deliveries. A good project begins with a discovery phase, then an MVP that solves a specific problem, then iterations to expand features. Each phase must include user testing, training and documentation.
Finally, cost. You have to compare the price of the platform, custom development, integrations, security, training and maintenance. ROI arrives when the solution reduces time and operational costs in a measurable way. An honest provider delivers a business case with hypotheses, risks and deadlines.
At Q2BSTUDIO we work this way: with a comprehensive vision that combines custom software, AI, AI agents, cybersecurity, AWS/Azure cloud and BI/Power BI. Our goal is to prevent the client from depending on us for every change, so we deliver administration portals and clear documentation. Evaluating providers takes time and judgment; we hope these guidelines help you make a solid decision.
