I want to build an end-to-end AI platform that turns our scattered data into clear, revenue-driving direction for both our main dealership and its child company. Here’s what the system needs to do: • Merge everything we know about customers—especially the CRM records—into a single, reliable source of truth. • Detect buying preferences in real time, then translate them into model, feature, and finance package recommendations we can act on instantly. • Track rival dealerships’ marketing campaigns so we understand what offers, channels, and messages are winning shoppers before they ever walk onto our lot. • Scan regional marketing and demographic data to surface untapped segments or locations and suggest the most cost-effective media mix. • Feed every insight into intuitive dashboards plus automated alerts that guide inventory, promotions, and even day-to-day management decisions for our child company. I already have raw data streams (CRM exports, web analytics, social ad metrics, inventory feeds, and public competitor data). What I need is the architecture, data pipelines, machine-learning models, and a clean interface that business users can navigate without technical help. Python, SQL, modern ML frameworks such as TensorFlow or PyTorch, and BI tools like Power BI or Tableau are all fine as long as they deliver fast, accurate output. Acceptance will be based on a working demo that ingests sample data, produces actionable recommendations, and shows measurable forecast accuracy against historical sales. Once that’s proven, we’ll agree on a phased rollout to production.