
Worked on the OpenCTI-Platform repositories to enhance natural language query filtering and document import capabilities. In the backend, refactored the Zod output schema and improved prompt engineering to increase the accuracy of extracting structured data from natural language inputs, enabling more efficient data discovery for users. Later, extended ImportDocumentAI to support simultaneous extraction of entities and relationships from imported documents, enriching threat intelligence graphs and improving data modeling. Leveraged TypeScript, Python, and Docker to implement these features, focusing on robust schema design, machine learning integration, and streamlined development environments to support reliable, scalable backend processing for cyber threat intelligence workflows.
OpenCTI-Platform/connectors – August 2025 monthly summary. Delivered a major data-enrichment capability by adding entity and relation extraction to ImportDocumentAI for document imports. This feature refactors processing to support simultaneous extraction of entities and relationships, enabling richer data import and more accurate threat intel graphs. Development environment improvements were implemented to streamline local setup and testing, and robustness was enhanced for STIX object creation and relationship validation across imports.
OpenCTI-Platform/connectors – August 2025 monthly summary. Delivered a major data-enrichment capability by adding entity and relation extraction to ImportDocumentAI for document imports. This feature refactors processing to support simultaneous extraction of entities and relationships, enabling richer data import and more accurate threat intel graphs. Development environment improvements were implemented to streamline local setup and testing, and robustness was enhanced for STIX object creation and relationship validation across imports.
Concise monthly summary for OpenCTI Platform (March 2025). Focus was on enhancing natural language query (NLQ) filtering in the backend to improve data extraction accuracy from natural language inputs and to lay groundwork for broader NLQ capabilities.
Concise monthly summary for OpenCTI Platform (March 2025). Focus was on enhancing natural language query (NLQ) filtering in the backend to improve data extraction accuracy from natural language inputs and to lay groundwork for broader NLQ capabilities.

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