Embed Conversational AI Chatbot for Portfolio Intelligence

Feature: Embed Conversational AI Chatbot for Portfolio Intelligence

Context & Background (For New Developers)
To give you some quick context on this ticket: REoptimizer® is a corporate real estate and lease management platform. Our users manage large portfolios of office spaces and facilities.

To help them make quick, data-driven decisions, we are introducing a conversational AI chatbot to the main desktop dashboard. Instead of making users manually filter through complex data tables, this AI will allow them to ask natural language questions about their portfolio and receive immediate, formatted insights.

Key Data Concepts You'll Work With:

Enterprise Leases: The database containing our users' active rental agreements, costs, and expiration dates.

Market Comps (Comparables): External/internal data showing the going market rate for similar properties in the same area. (Used to see if a user is overpaying).

Utilization Metrics: Data indicating how much of a leased space is actually being used (e.g., capacity vs. actual daily headcount).

User Story
As a portfolio manager using the REoptimizer® desktop dashboard, I want to interact with a conversational AI assistant using natural language, So that I can instantly retrieve complex financial, lease, and utilization insights without having to manually navigate and filter multiple reports.

Core Requirements
1. Data Source Integration
The chatbot backend must securely interface with three core data layers: Lease Data, Market Comps, and Utilization Metrics.

2. Natural Language Processing (NLP)
The system needs to interpret intent from business intelligence questions and translate them into actionable backend queries (e.g., Text-to-SQL or specific internal API calls).

3. Rich, Actionable Responses
The chatbot's UI cannot just return plain text blocks. It must be capable of rendering:

Precise Financials: Formatted rounded thousand dollar amounts (e.g., $15,000).

Comparative Math: Calculated percentage differences (e.g., 12?ove market average).

Deep-Linking: Active, clickable hyperlinks mapped to specific sites or leases. When clicked, these links must route the user directly to the detailed dashboard view for that specific property.

Technical Notes & Dependencies
Frontend UI: You will need to implement components for a collapsible chat window, message bubbles (with markdown/link parsing support), and typing/loading states. The UI must sit cleanly over the dashboard without obscuring critical widgets.

Backend / Middleware: Requires a routing layer to send user prompts to our designated LLM/NLP service. You will need to build the logic that handles the LLM's structured output, triggers the appropriate database queries, and formats the final response payload for the frontend.

Security (Critical): The AI must strictly adhere to our Role-Based Access Control (RBAC). Ensure the backend context limits the AI to querying and returning only the specific lease/site data that the currently logged-in user has permission to view.

Acceptance Criteria (UAT)
UI/UX Integration

Given a user is logged into the desktop dashboard,

When they click the AI Assistant icon,

Then a chat interface opens seamlessly on the screen without breaking or obscuring critical dashboard widgets.

Query Resolution & Data Integration

Given the user asks a natural language question about their portfolio,

When the question relates to lease data, market comps, or utilization,

Then the AI successfully targets the correct database and retrieves accurate, real-time data.

Response Formatting

Given the AI generates a response involving financial or comparative data,

Then the UI must correctly format currency symbols, accurately display calculated percentage comparisons, and present the data clearly for quick reading.

Dashboard Deep-Linking

Given the AI provides an answer concerning specific sites or leases,

When the response is rendered in the chat window,

Then it must include active hyperlinks that successfully navigate the user directly to the detailed, filtered dashboard view for that exact data point.

Required Test Cases
The assistant must successfully parse, query, and correctly answer the following test scenarios before this feature can be marked as complete:

"Where am I overpaying the most?"

Expected behavior: The system cross-references the user's current lease rates against the market comps database, calculates the largest negative discrepancies, and returns the top results.

"Which leases expire in the next 18 months?"

Expected behavior: The system filters the enterprise lease database by expiration_date