SEOdigital

Dimigas

We developed a fully customized logistics management platform designed to help companies organize and monitor their entire delivery operation from route creation to real-time fleet tracking.
The system was built for a logistics company that manages six active trucks, enabling complete visibility and control across daily operations.

The platform allows administrators to create and optimize delivery routes, assign drivers and vehicles, and add express or last-minute orders dynamically throughout the day.
Each driver accesses the system through a dedicated mobile app (iOS & Android), where they can view their route, mark completed deliveries, and update statuses in real time.
This synchronization ensures instant communication between the field and the main office.

In addition, the software includes tools to print daily delivery summaries, manage product inventory and stock, and analyze business performance through integrated KPI dashboards.
These dashboards provide clear visibility into operational efficiency, delivery times, and product flow, helping managers make better, data-driven decisions.

Key features include:

• Real-time GPS tracking of all active trucks.

• Route creation and optimization tools for efficient planning.

• Express order management for urgent deliveries.

• Driver mobile app (iOS & Android) with live synchronization.

• Stock and product control with automated updates.

• Performance KPIs and analytics for business insights.

• Daily summary printouts and digital reporting tools.

Built with a modern UX/UI and secure backend architecture, the platform offers scalability, stability, and complete operational control.
This solution helped the client reduce administrative work, optimize delivery routes, and gain full visibility over their logistics chain — all from a single, easy-to-use system.

Salud Libre

We developed an AI-powered virtual medical assistant designed to enhance patient access to healthcare services through intelligent conversational interaction. The solution integrates a Large Language Model (LLM) with a Retrieval-Augmented Generation (RAG) architecture to provide accurate, context-aware responses based on structured medical directory data.

The system enables users to search for doctors by medical specialty, geographic zone, or neighborhood, and receive personalized recommendations based on qualifications, availability, and service offerings.

Through natural language queries, users can ask questions such as:

• “Find a cardiologist near me.”

• “Which doctors specialize in pediatrics in this area?”

• “Who are the top-rated dermatologists available this week?”

• “What new services are available?”

The assistant processes the query using LLM reasoning capabilities and retrieves verified data from the platform’s medical database via RAG architecture, ensuring responses are grounded in real-time structured information.

Each recommendation includes:

• Direct profile access

• Medical specialty details

• Availability and working hours

• Location data

• Direct booking links

The system significantly improves navigation efficiency across the healthcare platform, reducing friction in doctor discovery and increasing appointment conversion rates.

Built on secure cloud infrastructure with role-based access and structured medical data indexing, the assistant transforms a static directory into an intelligent, conversational healthcare access layer.

This solution positions the client’s medical platform as a digitally advanced, AI-enabled healthcare ecosystem focused on accessibility, precision and user experience.