Intelligent Document Experience Assistant (IDEA)
An end-to-end document intelligence system that converts unstructured enterprise documents into structured summaries, key themes, and explicit risk signals, enabling faster comprehension, consistent review, and decision-ready insights through responsible use of LLMs.
Tags & Technologies
Key Impact & KPIs
- Reduced manual document review effort by surfacing concise summaries and key topics
- Explicit risk and gap signals extracted to support compliance and governance workflows
- Improved consistency of interpretation across technical and non-technical stakeholders
- Faster time-to-understanding for long-form reports and policies
- Reliable outputs under infrastructure and cost constraints
Project Overview
1. Modular Document Intelligence Pipeline
Designed a modular document intelligence pipeline that ingests heterogeneous inputs (PDFs and images), prioritizes native text extraction, and applies OCR only as a fallback—ensuring robustness, scalability, and alignment with real enterprise document ecosystems.
2. Prompt-Driven Document Understanding
Implemented prompt-driven document understanding using Mistral-7B, focusing on high-value analytical primitives—summarization, topic extraction, and explicit risk/gap identification—rather than open-ended generation, improving reliability and trustworthiness.
3. Careful Prompt Engineering
Applied careful prompt engineering and decoding controls (deterministic generation, bounded token limits) to reduce hallucinations and ensure repeatable, factual outputs, suitable for risk- and compliance-sensitive contexts.
4. Separation of Reasoning and Presentation
Separated reasoning from presentation, ensuring the LLM acts strictly as an interpretation layer and does not influence upstream extraction logic—preserving transparency and preventing opaque, end-to-end black-box behavior.
5. Interactive Demo-Ready Application
Delivered an interactive, demo-ready Streamlit application that operationalizes the pipeline for real-time document analysis, demonstrating how LLMs can be integrated responsibly into enterprise workflows under practical infrastructure constraints.
Model Selection Rationale
- Models/LLMs used: OCR + embedding retrieval (sentence-transformers) for grounding; Mistral-7B for contextual reasoning.
- Trust & grounding: Retrieval-first design (embeddings → LLM) reduces hallucination by anchoring responses to source text.
- Persona fit & cost: Mistral-7B provides strong reasoning capacity for persona-aware outputs while remaining cost-effective for enterprise use.