The GDPR detector service is a component of the IronClaw-powered Agent Mode feature. GdprDetector uses StorageService to scan project data for GDPR-related personal data indicators.

The IronClaw client service supports the IronClaw-powered Agent Mode implementation. The backend/app/services/ironclaw_client.py is a component of the IronClaw-powered Agent Mode. backend/app/services/ironclaw_client.py implements the IronClawClient used for remote agent communication via IronClaw Gateway API.

Visualization service is included as part of the IronClaw-powered Agent Mode feature.

The backend/requirements.txt has been updated to include Docling version 2.0.0 or higher as a mandatory dependency, while python-docx and python-pptx have been removed.

Performance optimization to process 50 chunks per GPU request, recently implemented.

Batch Upload uses the Batch Processing Strategy for extraction of files. Batch Processing Strategy uses RQ job queue for job management and reliability.

The batch upload pipeline workflow is validated by the batch-pipeline-workflow.spec.ts end-to-end frontend test case.

BatchProcessor uses FilePrioritizer to prioritize project files in processing pipeline orchestration. BatchProcessor uses ExtractionCoordinator to coordinate data extraction in the pipeline. BatchProcessor uses EmbeddingService to vectorize extracted data in the processing pipeline. DSStarService depends on BatchProcessor to orchestrate DS-STAR Agent API workflows. WorkflowService uses BatchProcessor to orchestrate analysis workflows and data processing pipelines. The File prioritizer is used by the Batch processor orchestrator in the processing pipeline. The Batch processor orchestrator uses the Question router to classify questions and route queries. BatchProcessor orchestrates the full pipeline that involves ExtractionCoordinator for extraction tasks. WorkflowService orchestrates pipelines that may be started and monitored by BatchProcessor. The batch processor orchestrator depends on the catalog worker as the first step in the batch processing pipeline. The batch processor orchestrator uses the prioritize worker to assign processing tiers. The batch processor orchestrator relies on the extract worker to perform data extraction from files. The batch processor orchestrator depends on the vectorize worker to generate embeddings for processed chunks. The batch processing pipeline orchestration is validated by the test_progress_status test case.

The DataLens Platform authenticates users using Bearer tokens instead of JWT.

DataLens requires lifecycle hooks like BeforeQuery and AfterQuery to support quota checking, cost tracking, and content filtering. DataLens requires BeforeQuery and AfterQuery lifecycle hooks for quota checking and cost tracking

DataLens requires adding BeforeQuery hooks to intercept request processing for quota checking etc.

Bridge Consulting worked as a consultant on the SVGV Budget Analysis Project.

In scope as a core functionality for data analysis; implementation pending.

Budget Analysis Cluster requires the Consolidation Mechanism to combine related budget tables for comprehensive queries.

Business process example involving multi-table joins for budget vs. actual expenditure analysis.

Budget questions are partially answerable from Excel files available after Phase 1 processing

Critical requirement for tracking batch cataloging progress, implemented successfully.

The chunking strategy for DOCX and PPTX files requires semantic chunking based on section or slide boundaries, improving retrieval quality for RAG.

The Data Discovery feature implementation was approved by stakeholder Claude. IronClaw Gateway integrates with Claude to provide natural language clarification and execute analysis steps in Danish. The Proof of Claude response in Danish validates the correct functioning of Claude in the system. The Proof of Claude response in Danish validates the correct functioning of Claude in the system. The Proof of Claude response in Danish shows usage of the DataLens budget database data in answers. The Proof of Claude response in Danish validates the correct functioning of Claude in the system. The Proof of Claude response in Danish validates the correct functioning of Claude in the system. The Proof of Claude response in Danish shows usage of the DataLens budget database data in answers. The Hybrid (manual + AI-Assist) Approach uses Claude to generate a detailed project goal from a brief description. The AI-assisted goal generation endpoint integrates with Claude to expand a brief description into a detailed project goal. IronClaw Gateway integrates with Claude, connecting users to LLM for clarifications and execution phases. Claude in the architecture is the Anthropic Claude large language model used remotely. OpenClaw Gateway integrates with Claude via the Anthropic API to process and respond to user queries. Claude successfully responded in Danish with a streaming response about the budget database during a previous successful session of DataLens OpenClaw Integration on March 23, 2026, 20:36.

The Cleveland Clinic E2E use case utilizes the PDF Infrastructure to handle extraction of PDF files.

DataLens Agent Mode supports a Cloud Model Backend using Anthropic API (Claude) for higher capability reasoning.

AI-powered column mapping suggestions are in development to help align uploaded data with standard schemas, facilitating cross-file queries and improving data consistency.

The Commit identification and deployment process is part of the decision to revert to the last known good commit and deploy. The Commit identification and deployment process involves using the Master branch for pushing the reverted commit. Auto-deploy via Coolify integrates with the Commit identification and deployment process to automate deployment after pushing the master branch code.

DataLens displays confidence scores indicating AI certainty for each query response. DataLens shows confidence scores alongside query results