AI Marketing & Stock Intelligence Platform

@devsrank

Hello, We would like to grab this opportunity and will work till you get 100% satisfied with our work. We are an expert team which have many years of experience on Python, NoSQL Couch & Mongo, Node.js, PostgreSQL, Elasticsearch, React.js, OpenAI, AI Development Please come over chat and discuss your requirement in a detailed way. Regards

@Jaydeep365

Hello, I have 9+ years of experience in Full-Stack Development, AI solutions, SaaS platforms, React.js, Node.js, Python, PostgreSQL, OpenAI APIs, TensorFlow, and cloud-based architectures. *Build the ad-automation platform using Python, TensorFlow, OpenAI APIs, React.js, Node.js, and PostgreSQL. *Integrate Google Ads, Facebook Ads, and LinkedIn Ads APIs for campaign data ingestion and optimization. *Develop ML pipelines for performance analysis, audience optimization, budget allocation, and automated recommendations. *Implement AI-powered creative generation and campaign experimentation workflows. *Build real-time React dashboards with KPI monitoring and automated/manual control modes. *Design scalable backend services with PostgreSQL, caching, and event-driven processing. *Prepare the foundation for Phase 2 market intelligence, sentiment analysis, technical indicators, and predictive analytics. *Deliver clean, documented code, unit tests, Docker deployment setup, and deployment guide. I have experience building AI-powered SaaS platforms, analytics dashboards, automation systems, and data-driven applications. I also have relevant examples to showcase. Let's connect first for a quick discussion so I can share similar work and outline a realistic architecture, timeline, and implementation approach. Regards, JB

@mrresearcher99

Hello, I have 10+ years of experience building scalable SaaS platforms, AI-powered applications, and full-stack solutions using Python, React.js, Node.js, PostgreSQL, TensorFlow, Docker, and cloud infrastructure. Your vision of combining ad-tech automation with market intelligence is both innovative and technically achievable. I can architect and develop the first-phase platform, including data ingestion from Google, Facebook, and LinkedIn Ads APIs, ML-based campaign optimization, automated budget allocation, AI-generated creatives and audience recommendations using OpenAI APIs, and a real-time React dashboard with manual and automated control modes. For scalability, I would implement a modular microservice architecture with Python-based ML services, Node.js APIs, PostgreSQL storage, event-driven processing, and containerized deployment using Docker. The system will include testing, monitoring, documentation, and CI/CD pipelines to ensure reliable production deployment. I can also provide a realistic development roadmap, architecture design, and milestone plan before development begins. Best regards

@iimtpradeepdash

Ad platform integration breaks most projects at the data layer—pulling consistent campaign results, then feeding them into a pipeline that isn't brittle when APIs change. I built a similar ad+AI product last year (PackSmart AI, under NDA), where attribution tracking and live dashboarding were the critical paths. For your v1, I'd design Python ETL services for social ad APIs, with TensorFlow for the ML (recommendation, lookalike building, creative scoring) and OpenAI APIs driving creative/text generation. React + Node for the dashboard, real-time stats by batching into Redis then syncing with PostgreSQL for history, Dockerized to deploy or scale up. Does your team have ad platform dev keys and test accounts, or do you need onboarding for each? That decides our API start point. I can map the architecture if this scope fits. Reply if you want the outline. Pradeep

@NEHABHAT92

As a seasoned AI developer, I understand the importance of building a platform that centralizes multiple data streams into one, cohesive system. I've honed my skills in Python, Node.js, and React.js over the course of 9+ years, which leave me well-equipped to merge data from Google, Facebook, LinkedIn and stock market feeds in a seamless manner. My comprehensive experience in mobile and web development will enhance my ability to take your cloud-based AI marketing solution to another level. In addition to proficiency with Python and Node.js, I am well-versed with TensorFlow and OpenAI APIs—essential technologies for your project. With experience in using these tools on large-scale platforms, I can build you an intelligent system that learns from historical ad data to launch experiments and re-allocate budgets accordingly. Moreover, my expertise extends to back-end modules such as data ingestion, sentiment analysis, running technical indicators and more. My focus has always been not just on developing projects but also ensuring they are user-friendly and stable. For your first release, not only will I provide a working ML pipeline that includes live optimization recommendations and/or automated actions but also a clean codebase with short deployment guide- all backed by unit tests. In terms of acceptance criteria, rest assured that I'll do everything possible to deliver coherent A/B testing results within weeks while maintaining low dashboard latency under concurren

@kishanbwts

I understand you need a cloud platform that tightly integrates ad automation with stock-market insights, starting with robust campaign optimization on Google, Facebook, and LinkedIn. I’ve built similar systems where live campaign data fed real-time ML models to improve ad targeting and budget allocation, resulting in measurable CTR lifts within weeks. For your ML pipeline, I suggest a modular approach where historical and real-time campaign data flow into retrainable TensorFlow models. We can use OpenAI APIs to generate creative variations dynamically, combined with precise audience refinement. On the React dashboard, metrics will update via websocket streams to ensure under-5-second latency, even under heavy load. A few questions: How frequently do you want the models to retrain? Is there preferred granularity for audience segments? Also, to support your A/B test goals, do you have existing labeled datasets or will we bootstrap from raw logs? I can have the first working ML pipeline and dashboard ready within 3-4 weeks, including test coverage and a Docker deployment guide. This foundation will meet your key acceptance criteria and scale well for phase two. I’m ready to start outlining the architecture and timeline as soon as you confirm priorities on retraining cadence and data availability.