Production Portfolio

REAL AI SYSTEMS I'VE BUILT

Each project focuses on a real business problem, sophisticated AI logic, and measurable results.

Screenshot of an LLM observability dashboard used for production AI feature delivery

AI Finance Sentiment Tracker

The Problem

Financial teams spending hours manually analyzing market sentiment across hundreds of news sources daily.

Data Pipeline

Scrapes 200+ financial news sources → NLP preprocessing → Sentiment classification model

AI Logic

Custom NLP pipeline classifies sentiment (bullish/bearish/neutral) with 94% accuracy. Time-series analysis detects trend changes before they become obvious.

Measurable Result

Real-time market sentiment dashboard with automated alerts. Reduced analyst research time by 70%.

NLPSentiment AnalysisReal-timePython
Screenshot of an AI workflow automation command center with approvals and integrations

Intelligent Automation System

The Problem

Businesses losing 20+ hours/week on repetitive decisions that follow patterns but require human judgment.

Data Pipeline

Incoming data → Intent classification → Decision engine → Workflow execution

AI Logic

AI decision engine evaluates incoming data, classifies intent using fine-tuned classifier, then executes appropriate workflow — emails, CRM updates, API calls, or escalation to humans.

Measurable Result

Autonomous decision-making pipeline handling 80% of routine decisions. Human-in-the-loop fallback for edge cases keeps accuracy at 99%.

Decision EngineWorkflowAutomationFastAPI
Screenshot of a private RAG document chat system with source citations

RAG Document Chat System

The Problem

Users need instant, accurate answers from their own documents without manually searching through hundreds of pages.

Data Pipeline

File upload → PDF/text extraction → Semantic chunking → Local embedding → In-memory vector store → LLM generation

AI Logic

Documents are chunked with context-aware overlap, embedded locally using all-MiniLM-L6-v2 via Xenova Transformers, stored in a session-isolated memory vector database, and queried via cosine similarity. Top chunks are fed to Groq's Llama 3.3 70B for grounded, source-aware responses.

Measurable Result

Live demo processing documents in under 3 seconds. Zero-cost stack using local embeddings and free-tier LLM. Session-isolated with automatic 30-minute data expiry for privacy.

RAGNext.jsGroqVector SearchXenova
Architecture

STANDARD PIPELINE

The blueprint I use to reliably go from raw data to intelligent action.

Your Data

Documents, databases, APIs

Embeddings

Vectorized for semantic search

Vector DB

Indexed for fast retrieval

AI

LLM reasoning + context

Action

Answers, emails, workflows

Have a similar challenge? Let's design an intelligent system that transforms your operations.

Let's Talk