Data Flow & Integration

Descipher is designed to seamlessly move data from various sources into a unified system, ensuring that researchers receive a complete and accurate view of their research topic. Here’s how the data flows through the system:

From Query to Task Distribution: When a researcher submits a query through the user interface, the Orchestration Engine receives it and breaks it down into specific tasks. These tasks are then assigned to the appropriate specialized AI agents.

Connecting to External Sources: Each AI agent; whether it’s the Literature, Patent, Collaboration, Funding, or Legal Agent—accesses external data via the Data Integration Layer. This layer handles connections to academic databases, patent records, funding platforms, and legal repositories. It also standardizes the diverse data formats from these sources, ensuring that the information is consistent and ready for processing.

Gathering and Normalizing Data: Once the agents collect their respective data, the information is returned to the Orchestration Engine. Here, data normalization takes place to ensure that all pieces of information align, regardless of their original format. This step is crucial for maintaining data quality and ensuring smooth further processing.

Synthesizing the Information: All normalized data is then passed to the Synthesis Module. Powered by GPT-4, this module integrates the varied inputs into one comprehensive report. It identifies key insights, highlights trends, and offers actionable recommendations, effectively turning raw data into a clear, actionable narrative.

Final Delivery and Storage: The complete report is delivered to the researcher through the user interface, ensuring easy access to insights. Additionally, the final data and reports are securely stored in the Storage and Database system for future reference. This ensures that all information is not only available in real time but also archived reliably for subsequent use.

Reliability and Error Handling: Throughout the entire process, robust error handling and dynamic updates are in place. This means that even if one data source experiences issues, the system can continue to operate smoothly by rerouting or updating data as needed.

In summary, Descipher’s data flow and integration process connects multiple external sources with internal components, ensuring a smooth and efficient journey from the researcher’s initial query to a comprehensive, actionable report.