AI Marketing & Stock Intelligence Platform

I want to bring a single, cloud-based platform to life that merges two worlds most businesses keep separate: digital-ad automation and stock-market intelligence. The first milestone centres on perfecting Google, Facebook, and LinkedIn campaign optimisation—everything else will build on that solid core.

Here is the vision in practical terms. Using Python, TensorFlow, OpenAI APIs on a React/Node.js front-end, the system should learn from historical ad data, generate new creatives and audiences on the fly, launch experiments, and continually re-allocate budget to the best-performing ads. At the same time, I need back-end modules that ingest market data, scrape news sentiment, run technical indicators, and push predictive signals into interactive dashboards. PostgreSQL will handle storage; the entire stack will deploy to a scalable cloud environment.

Deliverables for the first release
• A working ML pipeline that ingests campaign performance from Google, Facebook, and LinkedIn, retrains models, and sends live optimisation recommendations or automated actions.
• An initial React dashboard that visualises KPI shifts in real time and lets me toggle automated vs. manual control.
• Clean, well-documented code with unit tests and a short deployment guide (Docker or similar).

Acceptance criteria
1. A/B test results must show statistically significant lift in CTR or CPA within two weeks on at least one of the three ad platforms.
2. Dashboard latency for updated metrics should stay under five seconds with 10k concurrent events.
3. Codebase passes all tests and installs in a fresh cloud instance with one command.

Once this core is stable we will expand into full market-analytics, cross-asset sentiment scoring, and automated portfolio signals—but that is phase two. I’m ready to start as soon as you can outline a realistic timeline and model architecture.