
Nora Errouhly developed and enhanced a multi-agent AI system for the uktrade/redbox repository, focusing on scalable document processing, semantic search, and tabular data analysis. She designed agent orchestration frameworks and integrated LLM-driven workflows, enabling automated evaluation, summarization, and SQL-based analytics. Using Python, SQL, and Jupyter Notebooks, Nora implemented robust backend features such as streaming summarization, WebSocket-based messaging, and automated QA pipelines. Her work included refactoring for maintainability, prompt engineering for improved accuracy, and load testing with Locust. These contributions established reliable, modular foundations for data-driven insights and production-ready AI features, demonstrating strong depth in backend and AI engineering.

October 2025 monthly summary for uktrade/redbox focusing on delivering the Tabular Data Analysis Agent to enable SQL-based analysis of structured data, processing results, and integrating them into the multi-agent system to derive actionable insights from tabular sources. This work improves data-driven decision-making and lays the groundwork for scalable data analytics within the product.
October 2025 monthly summary for uktrade/redbox focusing on delivering the Tabular Data Analysis Agent to enable SQL-based analysis of structured data, processing results, and integrating them into the multi-agent system to derive actionable insights from tabular sources. This work improves data-driven decision-making and lays the groundwork for scalable data analytics within the product.
September 2025: Delivered an end-to-end Automated Evaluation Framework for Tabular Data and SQL Query Performance in uktrade/redbox, featuring notebooks for metrics, a financial-data evaluation script, and prompt formatting enhancements to clearly delineate SQL queries. This work enables repeatable benchmarking, faster iteration, and clearer interpretation of results, aligning with business goals of reliable SQL performance assessment.
September 2025: Delivered an end-to-end Automated Evaluation Framework for Tabular Data and SQL Query Performance in uktrade/redbox, featuring notebooks for metrics, a financial-data evaluation script, and prompt formatting enhancements to clearly delineate SQL queries. This work enables repeatable benchmarking, faster iteration, and clearer interpretation of results, aligning with business goals of reliable SQL performance assessment.
August 2025 performance summary for uktrade/redbox: Delivered a Tabular Data Evaluation Framework with SQLite backend and Locust-based performance testing. Implemented a Jupyter notebook workflow to download, process, and evaluate datasets via SQL queries against a generated SQLite database, with robust logging and error handling. Added WebSocket-based messaging and streaming support in the evaluation flow, and updated repository hygiene by improving data directory management via .gitignore. Set up load testing to exercise realistic user interactions and streaming responses, establishing a foundation for scalable data evaluation and production readiness.
August 2025 performance summary for uktrade/redbox: Delivered a Tabular Data Evaluation Framework with SQLite backend and Locust-based performance testing. Implemented a Jupyter notebook workflow to download, process, and evaluate datasets via SQL queries against a generated SQLite database, with robust logging and error handling. Added WebSocket-based messaging and streaming support in the evaluation flow, and updated repository hygiene by improving data directory management via .gitignore. Set up load testing to exercise realistic user interactions and streaming responses, establishing a foundation for scalable data evaluation and production readiness.
July 2025 performance summary for uktrade/redbox focused on reliability, scalability, and configurable routing. Delivered robust chat handling for incomplete data, extended long-lived WebSocket connections, and introduced a feature-flagged routing option to enable safe rollout of new routing logic. These changes strengthen real-time chat stability, reduce downtime, and enable safer feature experimentation with clear dependency/test updates.
July 2025 performance summary for uktrade/redbox focused on reliability, scalability, and configurable routing. Delivered robust chat handling for incomplete data, extended long-lived WebSocket connections, and introduced a feature-flagged routing option to enable safe rollout of new routing logic. These changes strengthen real-time chat stability, reduce downtime, and enable safer feature experimentation with clear dependency/test updates.
June 2025 (uktrade/redbox): Delivered the Question Evaluation Workflow to strengthen automated QA and accuracy for user-facing questions. No standalone bug fixes were logged this month; the feature work focuses on closing QA gaps and improving reliability. Impact: automated evaluation runs prior to the existing evaluator, enabling earlier QA checks and more accurate question handling, reducing manual QA time and enabling scalable processing. Technologies/skills: workflow orchestration via build_agent, new combine_question_evaluator, graph/pipeline updates, commit-driven development.
June 2025 (uktrade/redbox): Delivered the Question Evaluation Workflow to strengthen automated QA and accuracy for user-facing questions. No standalone bug fixes were logged this month; the feature work focuses on closing QA gaps and improving reliability. Impact: automated evaluation runs prior to the existing evaluator, enabling earlier QA checks and more accurate question handling, reducing manual QA time and enabling scalable processing. Technologies/skills: workflow orchestration via build_agent, new combine_question_evaluator, graph/pipeline updates, commit-driven development.
May 2025 performance summary for uktrade/redbox: Delivered streaming multi-agent summarization with activity logging, prompt optimizations, and robust handling of streaming outputs. Refactored the summarization workflow to support streaming responses, clarified agent responsibilities, renamed internal/external retrieval agents for clarity, and added an evaluation notebook for planner prompts. Implemented file upload reliability improvements by upgrading the unstructured API and adopting django-chunk-upload-handlers for S3 uploads. These changes reduce end-to-end latency, improve reliability in document uploads, and enhance maintainability and observability for ongoing prompt and planner improvements.
May 2025 performance summary for uktrade/redbox: Delivered streaming multi-agent summarization with activity logging, prompt optimizations, and robust handling of streaming outputs. Refactored the summarization workflow to support streaming responses, clarified agent responsibilities, renamed internal/external retrieval agents for clarity, and added an evaluation notebook for planner prompts. Implemented file upload reliability improvements by upgrading the unstructured API and adopting django-chunk-upload-handlers for S3 uploads. These changes reduce end-to-end latency, improve reliability in document uploads, and enhance maintainability and observability for ongoing prompt and planner improvements.
April 2025: Delivered a Multi-Agent Orchestration and Summarization Framework that decouples planning from execution across specialized agents (Document Agent, External Data Agent), enabling modular, scalable AI-driven workflows for document processing and data integration. Implemented a new summariser route and multi-agent state management to support parallel task execution. Enhanced the Evaluator with logging, prompt refinements, and a citation toggle, including an activity log and code cleanup. No major customer-facing bugs were recorded this month. These contributions improve delivery velocity, traceability, and maintainability, and establish a solid foundation for future enhancements.
April 2025: Delivered a Multi-Agent Orchestration and Summarization Framework that decouples planning from execution across specialized agents (Document Agent, External Data Agent), enabling modular, scalable AI-driven workflows for document processing and data integration. Implemented a new summariser route and multi-agent state management to support parallel task execution. Enhanced the Evaluator with logging, prompt refinements, and a citation toggle, including an activity log and code cleanup. No major customer-facing bugs were recorded this month. These contributions improve delivery velocity, traceability, and maintainability, and establish a solid foundation for future enhancements.
March 2025 monthly summary for uktrade/redbox focused on delivering a demonstrator for Langgraph Supervisor Agent orchestration. Delivered a Jupyter notebook showing the supervisor agent coordinating a researcher agent for web searches and a coder agent for calculations to answer user queries. The notebook includes agent definitions, prompts, graph structure, and an end-to-end example execution, establishing a reusable framework for agent coordination and prompt-driven workflows. This work lays the foundation for automated knowledge retrieval and computation to improve response quality and reduce manual intervention. No major bugs were reported this month; the emphasis was on feature demonstration and framework setup for future productionization.
March 2025 monthly summary for uktrade/redbox focused on delivering a demonstrator for Langgraph Supervisor Agent orchestration. Delivered a Jupyter notebook showing the supervisor agent coordinating a researcher agent for web searches and a coder agent for calculations to answer user queries. The notebook includes agent definitions, prompts, graph structure, and an end-to-end example execution, establishing a reusable framework for agent coordination and prompt-driven workflows. This work lays the foundation for automated knowledge retrieval and computation to improve response quality and reduce manual intervention. No major bugs were reported this month; the emphasis was on feature demonstration and framework setup for future productionization.
February 2025 monthly summary for uktrade/redbox focusing on semantic search enhancement and relevance filtering. Implemented removal of the BM25 query in favor of embedding-based semantic search to improve recall and accuracy. Added a similarity score threshold to filter results; the KNN boost parameter was commented out to simplify ranking. Introduced a new notebook to analyze search optimization and LLM hallucination, and updated unit tests to reflect the new workflow and ensure only relevant documents are returned. Commits reflect bug fix and feature work addressing search relevance.
February 2025 monthly summary for uktrade/redbox focusing on semantic search enhancement and relevance filtering. Implemented removal of the BM25 query in favor of embedding-based semantic search to improve recall and accuracy. Added a similarity score threshold to filter results; the KNN boost parameter was commented out to simplify ranking. Introduced a new notebook to analyze search optimization and LLM hallucination, and updated unit tests to reflect the new workflow and ensure only relevant documents are returned. Commits reflect bug fix and feature work addressing search relevance.
January 2025 monthly summary for uktrade/redbox: Stabilized development OpenSearch querying by delivering a targeted bug fix in the development environment. The OpenSearch query handling issues were resolved, reducing errors and improving query processing performance. The fix is recorded in commit 21c1ef66820b4df52e01f3049baf1ad3a09f0ebb (Fix opensearch query dev (#130)). This work reduces dev-time troubleshooting, accelerates feature validation in QA, and lays groundwork for production parity.
January 2025 monthly summary for uktrade/redbox: Stabilized development OpenSearch querying by delivering a targeted bug fix in the development environment. The OpenSearch query handling issues were resolved, reducing errors and improving query processing performance. The fix is recorded in commit 21c1ef66820b4df52e01f3049baf1ad3a09f0ebb (Fix opensearch query dev (#130)). This work reduces dev-time troubleshooting, accelerates feature validation in QA, and lays groundwork for production parity.
Overview of all repositories you've contributed to across your timeline