In this project, you’ll build your first AI agent: a Moodboard Generator that turns simple prompts like “modern living room with warm colors” into visuals. By setting up your workspace in n8n and running your first workflow, you’ll see how ideas can instantly become outputs. What feels like a creative experiment is actually the foundation of Wayfair’s edge: turning abstract trends into concrete visuals fast. This hands-on win sets you up to build the more advanced agents that will track market shifts, monitor competitors, and generate insights that matter for Wayfair..

In this project, you’ll teach your agent how to think like a market analyst, spotting product trends before they go mainstream. You’ll build an AI system that scans real-world data sources such as Amazon and design blogs to identify what’s rising in popularity and why. Your goal is to build the foundation of a Consumer Trend Discovery AI Agent that helps Wayfair detect emerging design and product trends faster than traditional research teams, the kind of insight that drives smarter merchandising and faster product decisions.

In this project, you’ll shift from trend discovery to competitive intelligence: teaching your agent to think like a market strategist. Your agent will collect and analyze competitor product listings, new launches, price movements and reviews. The goal: to uncover what competitors are doing differently , how their customers are responding, and where Wayfair’s whitespace opportunities might lie. By the end of this project, your Competitor Monitoring AI Agent will surface actionable insights helping teams benchmark Wayfair’s products, spot early shifts in market positioning, and make faster, data-backed merchandising and marketing decisions.

In this project, you’ll evolve your existing agent into a marketing intelligence system, one that uses real trend and competitor data to generate ideas Wayfair’s content team could actually act on. You’ll work with a pre-built AI Insights & Content Agent and focus on refining its logic, creativity, and tone to produce sharper, brand-aligned outputs. By the end, you’ll have an agent that delivers on-brand blogs, captions, and campaign ideas, each grounded in real insights and built to drive marketing impact for Wayfair.

Before AI can process documents, it must first understand them. In this project, you’ll explore Machine Learning, Deep Learning, LLMs, and NLP to see how AI reads and processes text. You’ll also dive into Computer Vision and OCR, learning how AI extracts information from scanned documents. Since this externship focuses on mortgage automation, you’ll study the mortgage industry, its challenges, and why accurate document handling is essential. Finally, you’ll work with a real mortgage "blob" file, analyzing how AI models handle structured and unstructured data. By the end, you’ll have a strong AI foundation and a clear understanding of how AI improves document processing in the mortgage industry.

AI models require clean, structured data to function effectively. In this project, you’ll learn how to use Python for data processing, cleaning, and organizing mortgage documents to prepare them for AI automation. You’ll begin by exploring Google Colab and learning Python basics. Then, you’ll dive into data preparation techniques to clean and standardize information. Finally, you’ll work with image processing to enhance OCR (Optical Character Recognition) accuracy. By the end of this project, you’ll be able to prepare mortgage data for AI models, ensuring accurate and efficient automation.

Before AI can make sense of documents, it needs clean, structured data. In this project, you’ll learn how to turn both digital and scanned PDFs into machine-readable formats using Python. You’ll explore tools like PyMuPDF and pdfplumber to extract text from multi-page mortgage files. Then, you’ll apply field heuristics—like regex patterns, anchor phrases, and layout clues—to identify key fields such as borrower name, salary, and loan amount.

OCR (Optical Character Recognition) is at the heart of document intelligence. In this project, you’ll get hands-on with three OCR engines— Tesseract, PaddleOCR, and EasyOCR —to see how they perform on complex mortgage documents. You’ll start by learning how to clean and extract text from scanned PDFs using Tesseract. Then, you’ll explore layout-aware tools that better preserve formatting and structure. Finally, you’ll run comparisons and recommend the most effective tool for real-world automation. By the end, you’ll be able to confidently select and justify OCR tools based on both technical and business needs.

AI models can process vast amounts of text, but finding the right information quickly is a challenge. In this project, you’ll learn how to build a Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex to help AI retrieve relevant data from large document sets.

In this project, you’ll take your RAG pipeline to the next level by improving precision and experimenting with open-source LLMs. First, you’ll explore advanced chunking techniques—like overlapping and tuning—to improve document segmentation. Then, you’ll implement metadata-based filtering so your system retrieves more contextually relevant answers. You’ll also evaluate the performance of open-source models like Mistral and Phi-2 compared to proprietary options. By the end, you’ll have a fine-tuned, high-performing RAG pipeline optimized for real-world mortgage documents and the ability to justify your design decisions based on retrieval accuracy and model efficiency.

Mortgage files often come as massive, unstructured "blobs" that include dozens of document types in a single PDF. In this project, you’ll design a system to split unstructured blobs into individual documents, classify each one, and route them to the appropriate extraction logic. You’ll use layout-based clues and rule-based methods—or even simple machine learning—to separate and identify documents like pay slips, IDs, and contracts. Then, you’ll build a routing function to apply the right extraction pipeline for each document type. By the end, you’ll have created a modular, intelligent system that mirrors real-world document workflows in mortgage automation.

In this module, you'll learn how to turn your local RAG pipeline into an interactive chatbot using Gradio. Instead of running queries in a notebook cell, users can ask questions through a clean web-based UI — just like ChatGPT. You'll split your code into two functions: one that builds the Gradio interface, and one that handles user prompts by sending them through your retrieval engine and LLM. This structure not only helps modularize your pipeline, but also makes it easy to plug in different models — including Gemini, Mistral, Phi-2, or any LLM of your choice. By the end, you'll have a working local chatbot that can answer questions about a PDF document.

In this project, you’ll use a mix of AI tools to uncover major themes in employee feedback and dig into the root causes behind key frustrations. You’ll visualize how sentiment varies by theme and map out cause-effect patterns, just like consultants do when turning noise into strategy.

[Week] 5-6