AI-Powered Applications

Overview

AI is no longer a research project — it's a product feature. I build web and mobile applications that embed large language models (LLMs) to make your product smarter: conversational interfaces, document intelligence, automated content generation, and workflow acceleration.

Every implementation is production-focused — not just a ChatGPT wrapper, but a well-engineered system with proper context management, cost control, and safety guardrails.

What I Build

Conversational Interfaces

• Domain-specific chatbots trained on your product's context, FAQs, or documentation
• Multi-turn conversation with memory — the assistant remembers earlier messages
• Escalation to human support when the AI isn't confident
• Embedded directly in your web or mobile app, not a third-party widget

Document Intelligence (RAG)

• Upload PDFs, Word docs, or CSVs and ask questions in plain English
• Retrieval-Augmented Generation (RAG) — the AI searches your documents before answering
• Accurate citations — answers reference the exact document and section
• Use cases: legal document review, policy Q&A, knowledge base search, research assistant

Content & Text Generation

• Automated email drafting, proposal generation, report writing
• Product description generation from specs or SKU data
• Meeting summary and action item extraction
• Translation and tone rewriting

Data Analysis & Enrichment

• Classify, tag, or categorize large datasets automatically
• Extract structured data from unstructured text (invoices, emails, feedback forms)
• Sentiment analysis and topic clustering on customer feedback

Tech Stack

LLM Providers

• OpenAI (GPT-4o, GPT-4-turbo, GPT-3.5)
• Anthropic Claude — for longer context and safer outputs
• Mistral / Llama — open-source options for on-premise or cost-sensitive projects
• Streaming responses for real-time, typewriter-style chat UX

RAG & Vector Search

• LangChain or LlamaIndex — for document loading, chunking, and retrieval pipelines
• Pinecone / Weaviate — managed vector stores for semantic search
• PostgreSQL + pgvector — if you prefer keeping everything in one database
• OpenAI Embeddings for converting text to vectors

Backend & Orchestration

• Node.js (Express / Fastify) — fast, event-driven AI API layer
• Python (FastAPI) — when using Hugging Face models or heavy ML dependencies
• Streaming endpoints with Server-Sent Events (SSE) for real-time responses
• Rate limiting, token budgeting, and cost tracking per user/session

Frontend Integration

• Smooth streaming chat UI with React
• File upload and processing with progress indicators
• Markdown rendering for AI responses (code blocks, lists, headings)

Safety & Reliability

• System prompt design — AI constrained to your domain, not a general-purpose chatbot
• Guardrails — content filtering, profanity blocking, off-topic detection
• Fallback handling — graceful degradation when the API is unavailable
• Context length management — conversation history trimmed and summarized to stay within token limits
• Logging — all AI interactions logged for debugging and quality review

Delivery & Deployment

• Sandbox / staging environment with test data before production
• Environment-based API key management (dev/prod keys separated)
• Deployment to Vercel, Render, Railway, or custom VPS
• AI feature toggles — turn features on/off without redeployment
• Monitoring setup — latency, error rate, token usage dashboards
• Full handover with prompts documented and editable by your team