Overview
The AI Email Deliverability Intelligence Platform is an end-to-end AI-powered email analytics platform that predicts email deliverability using XGBoost, provides SQL analytics, stores prediction history in MySQL, and includes an offline AI assistant powered by Llama 3.2 (Ollama). It helps marketers and analysts evaluate campaign quality before sending emails by combining Machine Learning, SQL Analytics, MySQL, and Generative AI into one interactive application. The platform predicts email deliverability before campaign launch, analyzes campaign performance using SQL, maintains historical prediction records, answers technical questions using an offline AI assistant, and aims to improve sender reputation through data-driven decisions. The machine learning workflow includes data acquisition, profiling, feature engineering, cleaning, ordinal encoding, XGBoost model training, evaluation, Streamlit deployment, and prediction storage in MySQL. The application features an interactive Streamlit interface for AI deliverability prediction, a SQL analytics dashboard for client performance, open rate analysis, bounce rate metrics, complaint analysis, and business KPIs, as well as a prediction history module that stores every prediction in MySQL with search, filtering, CSV export, and history management. An offline AI chat assistant powered by Llama 3.2 and Ollama supports questions about email deliverability, SPF/DKIM/DMARC, SQL, Python, machine learning, campaign analytics, and general technical concepts without requiring internet or OpenAI credits.
Pricing
Free
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Use Cases
- Marketers use the platform to predict email deliverability before campaign launch.
- Analysts use the platform to analyze campaign performance using SQL.
- Teams use the platform to maintain historical prediction records.
- Users use the offline AI assistant to answer technical questions.
- Organizations use the platform to improve sender reputation through data-driven decisions.
- Users use the SQL analytics dashboard to analyze client performance, open rates, bounce rates, and complaints.
- Teams use the prediction history module to search by client category, filter by prediction status, and export history as CSV.
- Users use the offline AI chat assistant to ask questions about email deliverability, SPF/DKIM/DMARC, SQL, Python, machine learning, campaign analytics, and general technical concepts.
Features
Predict email deliverability with XGBoost
Analyze campaign data with SQL
Store prediction history in MySQL
Chat with an offline Llama assistant
Explore analytics through Streamlit
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