Analysis Recommendations generation uses the project scope and file catalog to suggest specific analysis questions. The insights table is the data source that Analysis Recommendations build upon to generate actionable cards using the project's goal context.

The Phase 2 MVP includes the Analysis Report deliverable documented in ANALYTICAL_RESULTS.md file.

The unified analysis page is validated by the analysis.spec.ts frontend test case.

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

Dependencies injected into each tool call within backend/app/services/pydantic_agent.py, for data analysis.

This requirement was not met; the formal verification of queries in the system has not yet achieved compliance.

Initially unspecified; planned to generate user answers based on improved schema and query understanding.

AnswerSynthesizer leverages Qwen3 to synthesize human-readable answers in Danish or English from SQL query results. Multi-Stage Text-to-SQL Architecture realizes the AnswerSynthesizer use case for generating human-readable answers with source attribution.

ANTHROPIC_API_KEY is required to be set in Coolify environment for OpenClaw to operate successfully without timeout. OpenClaw Gateway requires the Anthropic API key to call Claude and generate responses. OpenClaw Gateway uses the Anthropic API key from environment or config to authenticate calls to the Anthropic API. The Anthropic API key must be set in the elin environment for the OpenClaw Gateway to authenticate API calls to Anthropic. The Anthropic API key is obtained from the Anthropic Console by user registration and key creation. The Anthropic API key was added to the elin environment and resulted in successful OpenClaw Gateway authentication and Claude response. ANTHROPIC_API_KEY is required to be set in Coolify environment for OpenClaw to operate successfully without timeout. OpenClaw Gateway requires the Anthropic API key to call Claude and generate responses. OpenClaw Gateway uses the Anthropic API key from environment or config to authenticate calls to the Anthropic API. The Anthropic API key must be set in the elin environment for the OpenClaw Gateway to authenticate API calls to Anthropic. The Anthropic API key is obtained from the Anthropic Console by user registration and key creation. The Anthropic API key was added to the elin environment and resulted in successful OpenClaw Gateway authentication and Claude response.

API endpoints for discovery and validation are defined; next steps involve final integration. The API LAYER uses QuestionRouter for query routing in analysis endpoints. API LAYER is implemented using FastAPI framework for backend services. API Layer is implemented with FastAPI as the backend web framework. The API Layer contains the backend/app/api/analysis.py file. The backend/app/main.py file starts the FastAPI app that exposes the API Layer. FastAPI framework implements the API Layer.

The Architecture includes all Python and AI components running on elin. The Architecture includes all Python and AI components running on elin. The Architecture supports and integrates with the DataLens OpenClaw Integration to provide a solid system structure.

Arne Hauge is a verified user with access to Project 14. Arne Hauge is the stakeholder who will use the Data Discovery Feature for real-world analytics and user onboarding. Arne Hauge accesses data stored in the PostgreSQL project_14 schema for budget analysis. Stakeholder Arne Hauge is designated to be onboarded and introduced to the Data Discovery Feature. Arne Hauge is expected to use the Data Discovery system after deployment.

The Ollama GPU qwen3-coder-next 80B model is used by the async embedding queue to generate GPU embeddings for text chunks. The Nomic-embed-text embedding model is used by the async embedding queue for batch GPU embedding processing. The file extraction process triggers the async embedding queue to generate embeddings asynchronously after extraction completes.

Implement diagnostic logging in question_router.py to track if event_generator is called, identify where execution stops, and pinpoint silent failures in async generators, especially in _run_query() or route() method, to improve visibility of exceptions.

Vanna 2.0 maintains audit logs tracking every query per user for compliance purposes. Vanna 2.0 tracks every query per user for compliance through audit logs. Vanna 2.0 records audit logs tracking queries per user for compliance

IronClaw-powered Agent Mode provides capability for autonomous data analysis.

The Multilingual Support (Danish) epic involves modifying Backend files to implement language preference and LLM prompt injection.

Backend system supports autonomous data extraction, natural language querying, document RAG, and findings visualization. Recent updates include integrating findings generation into API and agent workflows, with components ready for deployment and testing. The consolidation entity is to be connected and used within the analysis pipeline. The analysis pipeline is planned to use a consolidated view entity when conduction analyses. The analysis pipeline uses Arctic to generate SQL queries over the unified schema.

The backend main application includes the IronClaw-powered Agent Mode feature. Discovery API router was registered in the backend app main.py to expose discovery endpoints. IronClaw agent back-end logic is integrated with the main app via API router registration. Discovery API router was registered in the backend app main.py to expose discovery endpoints. IronClaw agent back-end logic is integrated with the main app via API router registration.

The agent skills service contributes to the IronClaw-powered Agent Mode functionality. The backend/app/services/agent_skills.py module is part of the IronClaw-powered Agent Mode feature. The code in backend/app/services/agent_skills.py depends on backend/app/services/text_to_sql.py for SQL extraction functionality within agent workflows. backend/app/services/agent_skills.py depends on backend/app/services/findings_generator.py to generate findings after query execution in the agent workflow.

Agent warming service is included in the IronClaw-powered Agent Mode implementation. AgentWarmingService uses ExploreSchemaSkill to assemble warm context by computing schema profiles.

The Data Discovery feature includes the Backend discovery service. The Backend discovery service requires the Entity extraction capability to process Danish questions. The Backend discovery service requires Table ranking to prioritize relevant tables. The Backend discovery service depends on Join discovery mechanisms to find table relationships. The backend app api discovery router is part of the Backend discovery service to provide API integration. The backend service discovery.py implements the API endpoints for the Data Discovery system.

findings_generator logging is to be implemented in backend/app/services/findings_generator.py. backend/app/services/agent_skills.py depends on backend/app/services/findings_generator.py to generate findings after query execution in the agent workflow.