Virtual Situation Room

Over the past few weeks, the team has been focused on integrating and consolidating the various components of the Virtual Situation Room into a single, streamlined workflow. Integration Highlights Language Verification Agent Integration – We fully incorporated the agent into the main workflow, ensuring that content verification and AI-generated outputs maintain high quality across the system. Frontend-Backend Connection – The dashboard frontend is now fully integrated with backend services, allowing seamless display of processed data and AI-generated reports. Unified Pipeline Development – The team is consolidating modular agents and data components into a single pipeline, ensuring smooth operation from data ingestion, through processing, to final report generation and dashboard visualization. Preparing for Client Handover As the project approaches client delivery, the team was focused on making the system as user-friendly and reliable as possible: Single-Click Wor...

Development during this phase focused on strengthening the data infrastructure and backend capabilities that support the Virtual Situation Room.  A key milestone was implementing Azure PostgreSQL migration and integration into the core pipeline , allowing processed data and generated outputs to be stored and accessed more reliably across the system. In parallel, work began on building the backend application using Django , laying the foundation for a more robust application layer that connects the data pipelines, analytical services, and frontend interface. ReliefWeb Integration To expand the system’s information coverage, ReliefWeb was successfully integrated into the data ingestion agent, providing access to a broader and more consistent stream of crisis-related information. As a well-established humanitarian information platform, ReliefWeb offers structured and regularly updated reports from a wide range of trusted organisations, making it a valuable addition to the ingestion...

 A key focus during this phase of development has been tackling retrieval and processing challenges when working with large datasets in LLM pipelines . As the Virtual Situation Room ingests more information from diverse sources, we needed a way to process and analyze large volumes of data without exceeding model context limits. Ensuring that relevant information could still be retrieved, structured, and synthesized by the agent pipeline became a central technical challenge. To address this, we explored different approaches for handling large datasets in LLM workflows, including retrieval-based architectures and tools such as Azure AI Search . After evaluating several options, we collaborated to design and implement the full report generation pipeline , moving from architecture design through to a functional implementation. The solution we developed relies on a chunking-based approach , where large datasets are broken into smaller segments that can be processed sequentially and lat...

Development on the Virtual Situation Room has continued to focus on strengthening the AI pipeline that powers crisis analysis.  A major focus has been on building out the  data ingestion and analysis pipeline  that feeds the Virtual Situation Room. This work centers on creating reliable workflows for collecting, processing, and analyzing incoming data so that downstream agents can operate on structured, high-quality inputs. Strengthening this layer is critical to ensuring the system can scale as more data sources and analytical functions are integrated. Regarding the core agentic pipeline, we drafted  structured prompts for the report generation pipeline , which will guide how agents synthesize collected information into structured analytical outputs. These prompts are designed to enforce consistency in how findings, context, and signals are organized within generated reports. These generated reports will follow the IFRC Analytical Framework as shown in the images ...