CEO Dashboard Conversational AI MVP I want to build a minimum-viable conversational AI module that lets a CEO speak naturally to a chatbot and immediately receive a concise overview of the company, even though—for now—the data will be mocked. The bot must be able to: • Summarize day-to-day operations in plain language (English & Arabic) • Surface key performance indicators drawn from mock financial, employee-performance, and sales datasets. • Handle ad-hoc follow-up questions, maintaining context across turns. • Deliver answers as a hybrid of text explanations and simple visuals—think inline charts or mini-dashboards generated on request. You are free to pick the stack that gets us there fastest; Python with LangChain/FastAPI and a lightweight front end (Streamlit, React, or similar) would be ideal, but I’m open to alternatives if they shorten development time. The mock data can live in JSON, CSV, or an in-memory database—whatever keeps setup friction low—yet the code should be written cleanly enough that real data sources can slot in later. Deliverables 1. Running MVP: an API or small web app that accepts natural-language prompts and returns both a text and visual response. 2. Mock dataset and clear instructions for replacing it with production data. 3. Read-me covering setup, endpoints, and a quick demo script or screen capture. 4. Brief note on how you would scale the model (e.g., vector store, caching) once it moves beyond MVP. Acceptance criteria – All three data domains reflected in responses. – Visuals render without manual refresh. – Follow-up queries maintain conversational context for at least three turns. – Local install and run confirmed on my machine in under 15 minutes. If this aligns with your expertise, let’s get a prototype in place quickly so we can iterate toward production. example conversations: "CEO, here's your 60-second overview:" Student pass rates hit record high: 92% this week Customer satisfaction improved by 18% this month Qatari hires goal achieved 2 months early 2 ALERTS NEEDING ATTENTION 3 top instructors at high risk of leaving next month - Impact: QAR 450,000 in revenue at risk - Action: Ready retention packages Customer service capacity at 110% this afternoon - Impact: 15% longer wait times - Action: Deploy 2 backup agents 1 OPPORTUNITY Weekend demand underestimated by 25% Action: Add 3 extra instructors on Fridays Want me to fix any of these now? Predictive analysis: Question: "Who might leave next month?" "Instructor #28 has 85% probability of leaving in 30 days. Reason: Salary 18% below market. Retention cost: QAR 25K vs Replacement cost: QAR 65K. Recommendation: Offer 12% increase now." Question: "Where are we wasting money?" "Found QAR 280,000 in annual savings: Probation failures cost: QAR 120K – Fix with better onboarding Vehicle underutilization: QAR 85K – Optimize scheduling Overtime inefficiencies: QAR 75K – Better workforce planning Should I implement these fixes?"