Chat & Document Analysis Platform
Chat & Document Analysis Platform for Finance Program Department
Built an AI-powered Chat & Document Analysis Platform with three core capabilities: document upload and management, standalone document analysis, and conversational interaction with financial documents via a RAG-based chatbot. The platform supports multiple file types, integrates cloud and local LLMs, and enforces role-based access control.
Top Features
Conversational Interface
Upload & Process Documents
Analyze Documents
System Statistics & Analysis
User Management
The Problem: Inefficiencies in Student Support and Internal Knowledge Access
Student-Facing Challenge
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Slow Responses
Students experienced delayed responses to their queries due to email-based communication, especially during peak academic periods such as the start and end of quarters.
Staff-Facing Challenges
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Repetitive Questions
Staff had to repeatedly answer the same questions from students, consuming significant administrative time and effort. -
Fragmented Documentation
Information was scattered across multiple documents and web pages, requiring staff to manually search through large files to find accurate answers.
My Role
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Owned the end-to-end AI product strategy for the Finance Program Department, translating student and staff support challenges into a scalable AI-powered solution.
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Defined the system architecture for a Chat & Document Analysis Platform, covering document ingestion, NLP preprocessing, embeddings, vector database, and RAG-based conversational retrieval.
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Designed clear separation between student-facing query resolution and staff-facing knowledge management, ensuring the system addressed distinct user needs.
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Led AI system design decisions across model selection (cloud and local LLMs), prompt design, retrieval strategy, and response grounding.
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Scoped the MVP, prioritizing core features based on user impact and feasibility, and created a product roadmap to guide phased development and future enhancements.
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Planned and executed product and model evaluation, validating accuracy, relevance, and usability of AI responses across real finance program documents and student queries.
Key Skills:
AI Product Strategy | Problem Discovery | User Research | Persona Definition | AI Architecture | RAG | Prompt Engineering | Model Evaluation | MVP Scoping
Success Metrics
Query Response Time Reduction
Reduced average response time from days to real-time
Administrative Query Deflection Rate
Deflect 40–60% of repetitive student queries
Document Access & Retrieval Efficiency
Reduce document analysis time
by 60–70%
Key Learnings
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Building AI products is non-deterministic by nature and requires guardrails, evaluation loops, and recovery paths.
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Clear separation between document management, analysis, and conversation significantly improves usability and system clarity.
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Embeddings + vector databases are critical for trustworthy retrieval, not just raw LLM responses.
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AI product success depends as much on UX, access control, and trust as on model quality.
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Designing for academic departments requires simplifying complexity without oversimplifying outcomes.
Future Iterations
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Advanced Query Analytics
Add dashboards to track common student questions, unanswered queries, and content gaps to continuously improve documentation and responses.
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Multi-Department Expansion
Extend the platform to other academic departments (e.g., Accounting, MIS, Operations) using the same architecture with department-specific knowledge bases.
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Improved Document Intelligence
Enable cross-document comparison, version tracking, and policy change detection across finance program materials.