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The Problem: Legal, HR, and finance departments are buried under unstructured documents (contracts, resumes, invoices) and struggle to extract key information quickly.
The Solution: A specialized AI agent that securely ingests hundreds of pages of unstructured PDFs, extracts specific key-value pairs (like contract expiration dates or invoice totals), and outputs the structured data into a dashboard or spreadsheet.
Tech Stack: LlamaIndex, Python, AWS Textract / OpenAI Vision, Streamlit for the dashboard.
Key Features Highlight:
Optical Character Recognition (OCR) integration.
Structured JSON output generation from messy text.
Privacy-first architecture (can be showcased using open-source local LLMs like Llama 3).
The network for creativity
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Agentic Financial Document Intelligence & Transaction Reconciliation Engine
• Role: Lead AI & Systems Architect
• Deliverables: Multi-Modal PDF Parser, Hybrid Agent Orchestrator, Deterministic Math Engine, Audit Trail Logger
• Timeline: 3 Weeks
OVERVIEW
Financial institutions and back-office teams spend thousands of manual hours reconciling unstructured documents (loan contracts, invoices, compliance bylaws) with relational transaction ledgers. Standard LLM wrappers fail catastrophically in this domain due to mathematical hallucinations, version conflicts, and a lack of explainable audit trails.
I engineered an agentic document intelligence system that combines multi-modal PDF extraction with deterministic validation tools to automate complex cross-referencing with 100% computational precision.
THE PROBLEM & DIAGNOSIS
LLM Math Hallucinations: Generative models cannot be trusted to perform exact financial calculations, interest compounding, or tax audits.
Unstructured vs. Tabular Asymmetry: Document facts are scattered across messy layouts, whereas transaction logs live in strict relational schemas.
Document Version Discrepancies: Clauses change across revisions; systems must resolve conflicting policies before executing reconciliation.
KEY ARCHITECTURAL DECISIONS
• Decision 01: Separation of Reasoning and Execution
The LLM acts strictly as a reasoning and extraction layer, identifying entities and formulas. All financial calculations and reconciliations are handed off to deterministic Python execution sandboxes.
• Decision 02: Cross-Source Entity Disambiguation
Built a cross-referencing engine that maps unstructured text snippets directly to SQL transaction IDs using hybrid vector-keyword scoring.
• Decision 03: Fully Auditable Chain-of-Thought
Every validation output includes a step-by-step citation trail pointing to the exact page, bounding box, and ledger entry.
MEASURABLE RESULTS & IMPACT
• 0% computational hallucination rate achieved via deterministic code execution.
• 90%+ precision in entity extraction across multi-page, complex financial agreements.
• Production-grade audit trails satisfying compliance requirements.
TECH STACK
Python 3.11, FastAPI, Pydantic, LangChain, PyMuPDF, Pandas, PostgreSQL / pgvector, Docker.
Day 4 of learning and creating with AI.
Today, I created a UGC-style product campaign for AURA PRO, a fictional wireless earbuds brand.
The goal wasn't just to make the product look good. I wanted the video to feel natural, relatable, and believable — like a real person sharing why they chose the product.
From the product concept to visuals → AI model → storytelling → video, everything was built with AI.
Still experimenting. Still learning. Still getting better.
Day 4/30.
What do you think?
AI Engineer Adobe IllustratorChatGPTGoogle FlowaivideocreationugccreatoraiproductphotographyAI Video ProductionUGC Creation
The focus on believability is exactly right for UGC. A polished render is easy to notice, but natural pacing and human context are what make a product story feel trustworthy.