This is the Electi review. Today, multimodal AI for small and medium-sized businesses. What it actually does, where it delivers, and what to watch out for before you scale. Here is the core argument. Most SMEs already have multimodal data. They just don't treat it that way. Sales figures live in Excel. Customer complaints arrive by email. Product photos sit in a shared folder. Invoice PDFs pile up in administration. Every team sees a fragment. No team sees the whole picture. Multimodal AI is not a solution looking for a problem. It is a direct response to that operational reality. What does multimodal mean in practice? Traditional AI systems work in a single mode: text only, numbers only, images only. Multimodal AI processes multiple input types simultaneously text, images, structured data, documents, and looks for relationships between them. McKinse describes it as particularly suited to multi-sensory data environments. In business terms, that means a single analytical workflow can connect CRM records, support tickets, invoice PDFs, and shelf photos instead of treating each as a separate project. According to a 2025 market overview, computer vision-based solutions account for 35% of multimodal implementations, and cloud deployments represent 57% of the total. That tells you something useful. Most companies start narrow with images and documents before expanding. Where does real ROI appear? Three areas stand out. First, document intelligence. An SME receiving invoices in multiple formats can use multimodal AI to cross-reference document text, supplier history, and ERP order data simultaneously, flagging inconsistencies before a human ever touches the file. Second, risk and anomaly detection. Cross-referencing text, transaction logs, and visual attachments makes it harder for errors or fraud to hide across disconnected systems. Third, customer service. Combining ticket text, attachments, CRM history, and delivery data allows faster, more accurate case classification. Milvis identifies three current limitations worth naming. High computational intensity, difficulty contextualizing cross-modal data correctly, and poor generalization to real-world scenarios outside training. These are not reasons to avoid multimodal AI, they are reasons to start small and choose platforms with pre-optimized models. The implementation logic is straightforward. Start with a problem that occurs frequently, carries a visible cost when mismanaged, and requires at least two data sources to fully understand. Measure before you expand. If you cannot define what improvement looks like before you start, you will confuse activity with results. That's the review.