Upload annual reports and financial documents to interact with AI using Retrieval-Augmented Generation — ask any question, get grounded answers.
From document upload to intelligent Q&A in six steps.
Drop a PDF, DOCX, TXT, or CSV file directly into the report intelligence panel. Supports documents up to 20 MB.
The system automatically extracts all text from the document, preserving the structure of tables, paragraphs, and data.
Text is divided into overlapping semantic chunks to preserve context across sections and across page boundaries.
Each chunk is converted into a high-dimensional numerical vector using an AI embedding model.
All embeddings are stored in ChromaDB, a purpose-built vector database optimized for semantic similarity search.
When you ask a question, the most relevant chunks are retrieved and passed to the AI model to generate grounded, accurate answers.
Retrieval-Augmented Generation (RAG) is an AI technique that combines the power of large language models (LLMs) with a knowledge retrieval system. Unlike a standard AI chatbot that relies only on its training data, RAG retrieves relevant information from your specific document and uses it as context when generating answers.
Can only answer based on training data (knowledge cutoff). Doesn't know the contents of your specific document. May hallucinate specific financial figures.
Reads your actual document in real-time. Answers are grounded in the document's content. Accurate specific figures, quotes, and data directly from the source.
Text is mathematically converted into vectors — arrays of numbers that capture the semantic meaning of the text. ChromaDB stores these vectors and uses cosine similarity to find the most relevant sections for your query.
Each text chunk is encoded as a 1,536-dimension vector. Similar topics cluster together in this high-dimensional space, enabling precise semantic search — not just keyword matching.
Sample questions investors ask when analyzing annual reports.