Fundamental Analysis
Overview
Financial analysis has traditionally been a manual, labour-intensive process, requiring analysts to sift through hundreds of pages of financial and analyst reports to extract key insights. Our AI-driven “Fundamental Analysis” solution revolutionizes this process by leveraging cutting-edge Retrieval-Augmented Generation (RAG), financial modeling, and AI-driven data extraction to provide a management consulting-level summary with 100% accuracy.
One of the core innovations of this solution is that it is fully LLM-agnostic, allowing flexibility in integrating OpenAI, Claude, Gemini, or any private LLM without rearchitecting the system. The thick RAG layer ensures that all analysis is grounded in factual financial data, making the AI responses verifiable and accurate.
Features:
Key features and capabilities
LLM-Agnostic Architecture – Designed to work with any underlying Large Language Model (LLM) without changing the core application logic.
Thick RAG Layer for Context-Aware Analysis – Ensures that AI-generated responses are always grounded in the extracted financial data.
Automated Financial Data Extraction – Processes up to 5 end-of-year financial reports and 1 analyst report to extract balance sheets, income statements, and management commentary.
Multi-Year Comparative Analysis – Converts figures into both SAR & USD, ensuring a standardized approach to financial evaluation.
Citation & Source Tracking – Every extracted figure is referenced back to its original report, ensuring full transparency.
Advanced Financial Analytics (“Commentary”) – Provides deep insights into Capex trends, production volumes, revenue segmentation, investment theses, and competitive positioning vs global IOCs.
AI-Powered Chat Assistant (“Analyse”) – Allows users to ask complex financial questions and receive clear, data-backed responses.
Exportable Financial Data – Users can export structured financial data in Excel format for further analysis.
Discrepancy Handling Logic – Prioritizes reported figures over AI-calculated values in case of discrepancies, maintaining data integrity.
Applications:
List Real-world examples
Investment & Asset Management Firms
Helps portfolio managers and research analysts process earnings reports faster
Management Consulting Firms
Assists consultants in performing detailed financial due diligence.
Corporate Finance Teams
Enables CFOs and financial planners to extract key insights quickly.
Private Equity & Venture Capital
Helps investors analyze financials before funding decisions.
Energy & Commodities Sector
Useful for IOCs, NOCs, and commodity firms analyzing financial trends.
Problem Statement
Challenges in Traditional Financial Analysis
Manual & Time-Consuming – Analysts spend days or weeks extracting data and building reports.
High Risk of Human Error – Manual extraction can lead to inaccuracies.
Lack of Standardization – Different financial reports have varied formats, making comparisons difficult.
Limited Scalability – As data grows, manual analysis becomes increasingly inefficient.
Our Approach
To solve these challenges, we designed a bionic AI solution that integrates document intelligence, financial modelling, and interactive AI-driven analysis into one seamless system. The architecture was deliberately made LLM-agnostic, ensuring compatibility with OpenAI GPT, Claude, Gemini, or in-house LLMs without modifying the underlying system.
1. Thick Retrieval-Augmented Generation (RAG) for Context-Aware AI
- Developed a robust RAG layer that directs all AI-driven analysis.
- Ensured that every AI-generated response is grounded in extracted financial reports, eliminating hallucinations.
- The RAG layer dynamically selects relevant financial data, making AI-generated insights contextually rich.
2. Intelligent Document Processing & Extraction
- Trained NLP models to extract structured financial data from unstructured PDFs, Word files, and scanned documents.
- Implemented table recognition algorithms to correctly parse balance sheets, income statements, and footnotes.
3. Advanced Financial Insights & Commentary
- Developed an AI-driven financial analytics module to analyze trends in Capex, revenue, and market positioning.
- Integrated comparative analysis models to benchmark financial performance against industry peers.
4. AI Chat Assistant for Deep-Dive Insights
- Built an interactive Q&A layer (“Analyse”) where users can query the AI for detailed financial analysis and comparisons.
- Integrated multiple LLM providers to allow organizations to switch between models without changing workflows.
5. Ensuring Data Accuracy & Compliance
- Designed an automated validation framework to cross-check extracted data against reported figures.
- Maintained full audit trails and citations for regulatory and compliance needs.
Tech Stack Used
Front End
React Js
Tailwind CSS
Redis
D3 Js
Chart Js
Database
PostgreSQL
Mongodb
Pinecone
Back End
FastAPI
Python
PyMuPDF
Open AI GPT
Elasticsearch
LLM & Retrieval-Augmented Generation (RAG) Layer:
LLM Providers: OpenAI GPT, Anthropic Claude, Google Gemini (Interchangeable LLM setup) Context Management: Thick RAG Layer (Retrieves and structures data before LLM interaction) Retrieval System: Elasticsearch & Pinecone (for fast document search) AI Query Orchestration: LangChain (Ensures LLMs only generate insights based on retrieved data)
The tools used
- Data Extraction & Processing: OCR (Tesseract), PDFMiner
- Cloud Hosting & Compute: AWS (S3, Lambda, EC2)
- Logging & Monitoring: Prometheus, Grafana
- CI/CD Pipeline: GitHub Actions, Docker