Many view government technology as something cold—a mere combination of servers, pipelines, and lines of code. But at UrbaDigital, we’ve learned that behind every data point on a dashboard, there is a citizen’s story waiting to be addressed.
A timely piece of data in the Unidad de Registro Social (URS) is an intervention that determines the well-being index of millions of households, building a bridge to better opportunities, social programs, and state subsidies.
The Context: The Challenge of Bottlenecks in Social Allocation Measuring impact on a national level presents a monumental technological challenge. The URS had a database of 10.9 million records, but the 85-question web form presented usability barriers for populations in extreme poverty with low digital access.
This massive volume of information faced manual ETL processes that generated bottlenecks of up to 8 hours, extensive manual reviews, and a data architecture lacking centralized orchestration. The challenge for UrbaDigital was clear: consolidate these processes into an optimized digital ecosystem, because “the processing of information must be transparent; using simple and clear language.”
Our Methodology: Transforming Data Chaos into Strategic Clarity To bridge the gap between complex raw data and accessible citizen services, UrbaDigital applied a comprehensive methodology structured in 4 fundamental pillars, achieving 11 technological improvements:
1. Process Automation and Agile Workflows Technology must serve the people using it. We replaced manual reviews with the automatic generation of credentials, workload assignment, and an initial application flow simplified to just three fields. We also implemented multi-channel notifications (email, WhatsApp, SMS).
2. Citizen Traceability System (UX/UI) Understanding that the platform would be used by diverse populations, we designed a Social Single Window with 19 tracking statuses, differentiated visibility by role, and proactive notifications, prioritizing a frictionless user experience.
3. Applied Artificial Intelligence To ensure high-quality information and real-time assistance, we deployed a conversational bot with GraphRAG architecture (Llama 7B + Neo4j) for internal queries, an FAQ chatbot for citizens filling out the form, and medical certificate analysis using LLMs.
4. Automated Data Pipeline Development (ETL) To guarantee the sustainability of the socioeconomic calculation, our engineering team modernized the infrastructure by implementing Apache Airflow as a centralized orchestrator, migrating scripts to Python, and integrating OpenStreetMap for geolocation, eliminating the manual processing of 80,000 monthly records.
Accelerating Digital Evolution: Scalable Tech Solutions for Government, Finance, and Enterprise
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