InsightService uses AnalysisSuggestion to generate smart analysis recommendations. InsightService generates AnalysisSuggestion using schema analysis via Ollama.

Integration requires adding the DiscoveryFlow component to the analysis page for front-end functionality. Integration requires registering and using the Backend service's API endpoints for discovery. Integration involves modifying backend/app/main.py to register discovery API routes. Integration requires modifying backend/app/services/question_router.py to incorporate consolidated views in query routing. Integration uses backend/app/services/consolidation.py for handling consolidated views in the analysis pipeline. The E2E test suite validates Integration by verifying all tests pass once components are wired together.

Intelligent consolidation is a capability within the Data Discovery feature. Intelligent consolidation is supported via the /api/v1/discovery endpoints (search, consolidate, preview). Intelligent consolidation uses TableIndex for semantic table matching. The Consolidation Mechanism produces Consolidated Unified Views by creating session-scoped joins of related tables for queries. Budget Analysis Cluster requires the Consolidation Mechanism to combine related budget tables for comprehensive queries. Payment & Commitment Cluster requires the Consolidation Mechanism to join payments and commitments data tables appropriately. Monitoring Cluster requires the Consolidation Mechanism to unify monitoring-related datasets for accurate analysis. Grant Administration Cluster relies on the Consolidation Mechanism for consolidating grant-related tables for analysis. The Data Consolidation capability creates TEMP VIEWs to unify related tables into a single schema for Arctic SQL generation. The Discovery Service implements the Schema consolidation mechanism to improve query success rate. The Schema consolidation mechanism is planned to be presented via the Schema relationship explorer UI for better transparency and customization. The Data Consolidation process stores consolidation recommendations for user reference. The Data Consolidation capability creates TEMP VIEWs to unify related tables into a single schema for Arctic SQL generation. The Discovery Service implements the Schema consolidation mechanism to improve query success rate. The Schema consolidation mechanism is planned to be presented via the Schema relationship explorer UI for better transparency and customization. The Data Consolidation process stores consolidation recommendations for user reference. Intelligent consolidation improves the query success rate from 70% to over 95%. The Data Discovery feature uses intelligent consolidation to enhance query success rate.

The IronClaw agent feature includes autonomous agent orchestration, UI navigation buttons, and frontend user interface components. It relies on the IronClaw agent tables for storing session and message data, with high implementation priority, now deployed and integrated into DataLens, enabling the agent to process questions in Danish and generate responses using the Arctic and SQLCoder models.

IronClaw onboarding process initializes the IronClaw database to enable session storage and persistent agent threads. IronClaw onboard process configures and initializes the IronClaw database to enable session and thread management.

Graphic depicting IronClaw's data persistence or architecture.

IronClaw-powered Agent Mode provides capability for autonomous data analysis. The backend API agent code is part of the IronClaw-powered Agent Mode implementation. The backend main application includes the IronClaw-powered Agent Mode feature. The agent models are part of the IronClaw-powered Agent Mode subsystem. The agent skills service contributes to the IronClaw-powered Agent Mode functionality. Agent warming service is included in the IronClaw-powered Agent Mode implementation. The GDPR detector service is a component of the IronClaw-powered Agent Mode feature. The IronClaw client service supports the IronClaw-powered Agent Mode implementation. Visualization service is included as part of the IronClaw-powered Agent Mode feature. Database migration scripts add tables for the IronClaw-powered Agent Mode. The ChatPane component is part of the frontend agent interface for IronClaw-powered Agent Mode. The FindingCard UI component belongs to the IronClaw-powered Agent Mode frontend features. The FindingsBoard component is part of the frontend UI for IronClaw-powered Agent Mode. The GDPR warning UI component is integrated into the IronClaw-powered Agent Mode frontend. MessageBubble component is part of the IronClaw-powered Agent Mode frontend interface. ModelToggle frontend component supports user interaction within IronClaw-powered Agent Mode. The agent store Svelte module manages state for IronClaw-powered Agent Mode frontend. The frontend route for agent page implements part of IronClaw-powered Agent Mode UI. The backend/app/api/agent.py file is part of the IronClaw-powered Agent Mode implementation. The backend/app/services/agent_skills.py module is part of the IronClaw-powered Agent Mode feature. The backend/app/services/ironclaw_client.py is a component of the IronClaw-powered Agent Mode. The frontend/src/lib/components/agent/ChatPane.svelte is a UI component for the IronClaw-powered Agent Mode.

IronClawClient.send_message() depends on SkillExecutor or corresponding executor to process messages and yield responses asynchronously.

Represents a message within the IronClaw conversation, managed through the ironclaw_client.py service class. _run_query generates findings streamed as 'insight' messages, each rendered as a separate IronClawMessage agent_skills.py process_message() yields IronClawMessage instances for communicating agent responses and errors.

Configuration data for an IronClaw agent session, handled by the ironclaw_client.py service.

IronClawSkills uses IronClawTools to provide skill definitions callable by IronClaw agent. IronClawToolRegistry registers tools that are used by IronClawSkills for agent skills execution. IronClawClient uses IronClawSkills definitions for agent skill execution via the IronClaw Gateway.

Manages registration of tools with the IronClaw gateway; located in backend/app/services/tool_registry.py. IronClawToolRegistry registers tools that are used by IronClawSkills for agent skills execution.

IronClawSkills uses IronClawTools to provide skill definitions callable by IronClaw agent.

Platform supports cross-file queries using standard schemas and AI mapping. No specific mention of current IT questions in messages.

Internal data analyst at Acme Corp, involved in platform development and testing, with medium influence in project decisions.

Jesper is a Copenhagen-based AI consultant and builder. Jesper is the key stakeholder responsible for approving the decision on Phase 2 Strategy Research & Decision Point Direct communicator Jesper approved progressing with AI Summary Generation and vectorize progress tracking fixes in the same session for accuracy. Direct communicator Jesper prefers the longer fix implementing vectorize progress tracking rather than just quick AI summary fixes. Jesper DevOps is responsible for executing the agent migration file 003_agent_tables.sql to enable the IronClaw agent database functionality. Jesper depends on Coolify for deploying and managing the backend container. DataLens agent is owned by Jesper. Phase 2 Strategy Research & Decision Point uses the opinion of Jesper who favors a single coherent strategy rather than patchwork in deciding file processing scope. The opinion of Jesper influences the Phase 2 Strategy Research & Decision Point by favoring one coherent strategy instead of a patchwork approach. Jesper is responsible for manually testing the IronClaw Agent Feature and verifying the deployment status. DataLens agent is owned by Jesper, an AI consultant based in Copenhagen.

DiscoveryService uses JoinPath to represent joins between database tables for consolidation.

DataLens Development incorporates JWT authentication in its security architecture.

Timeout issues during query classification are mitigated by switching to keyword classification which takes under 10ms compared to prior 120s LLM classification.

The Discovery Service applies the Known keys join strategy to discover joins with 95% confidence.

The User Language Preference capability requires the Language column in users table.

The stable state of DataLens OpenClaw Integration depends on the last known good commit before timeout/skill loading changes caused regressions. The last known good commit will be reverted, pushed to master branch and auto-deployed via Coolify as the next step to restore function.

Lazy Loading Implementation is part of the PDF Infrastructure feature set to enhance user experience during large PDF uploads.

Vanna 2.0 implements lifecycle hooks for quota checking, logging, and content filtering at request lifecycle points. Vanna 2.0 utilizes lifecycle hooks for quota checking, logging, and content filtering at request lifecycle points. Vanna 2.0 architecture uses lifecycle hooks for quota checking, logging, and content filtering