AI/ML Development & Integration
Harness the power of AI and machine learning. From RAG systems to intelligent chatbots, I build AI-powered features that create competitive advantages.
What is AI/ML Development?
AI/ML development integrates artificial intelligence and machine learning into your applications to automate tasks, enhance decision-making, and create intelligent user experiences. Unlike traditional software that follows pre-programmed logic, AI systems learn from data and adapt to new situations.
I specialize in practical AI implementation: building RAG systems that let AI access your data, integrating LLMs like OpenAI's GPT, creating smart chatbots, and automating complex workflows with AI agents.
Key AI Concepts I Work With
🔍 RAG (Retrieval-Augmented Generation)
Combines LLMs with your own data. The system retrieves relevant information from documents, databases, or websites, then generates accurate answers based on your specific content.
🤖 LLM Integration
Connect GPT-4, Claude, or open-source LLMs to your application. Handle prompt engineering, context management, token optimization, and API integration.
💬 AI Agents & Automation
Build autonomous agents that can take actions, make decisions, and solve complex problems. Ideal for customer support automation, research assistants, and workflow automation.
📄 Document Intelligence
Extract information from PDFs, images, and documents. Build systems that understand contracts, invoices, forms, and unstructured data at scale.
🎯 Fine-tuning & Custom Models
Fine-tune LLMs on your domain-specific data for better accuracy. Build custom models optimized for your specific use case.
⚡ Vector Databases & Embeddings
Implement semantic search using embeddings. Store and query vector data with Pinecone, Weaviate, or other vector databases.
How AI Solves Business Problems
❌ The Problem
Your business generates tons of data (documents, customer interactions, knowledge bases), but teams can't efficiently find answers, automate repetitive tasks, or scale customer support without hiring more people.
✓ The Solution
Implement AI systems that understand your data and can answer questions, automate tasks, and provide intelligent recommendations. AI agents handle routine work 24/7, while your team focuses on strategic work.
🎯 The Outcome
Reduce operational costs, improve response times, scale without proportional headcount increase, and unlock new revenue opportunities through intelligent features.
Real-World Use Cases
DocMind - Document Intelligence
Chat with your documents. RAG system that ingests websites and enables real-time Q&A.
View case study →Customer Support Automation
AI chatbots that handle support tickets, answer FAQs, and route complex issues to humans. Reduces support costs by 60%.
Contract Analysis
Automatically extract terms, identify risks, and summarize contracts. From hours to minutes per document.
Personalized Recommendations
AI that understands user behavior and preferences. Increase engagement and conversion through intelligent suggestions.
Technologies & Tools
LLM Platforms
- OpenAI (GPT-4, Embeddings)
- Anthropic (Claude)
- Open-source LLMs
- Fine-tuning APIs
Frameworks & Tools
- LangChain
- LlamaIndex
- Hugging Face
- CrewAI (Agents)
Vector & Data
- Pinecone
- Weaviate
- Supabase pgvector
- PostgreSQL
"AI isn't magic—it's a tool that works best when thoughtfully integrated into your business processes and combined with human expertise."
Frequently Asked Questions
What is the difference between RAG and fine-tuning?▼
RAG (Retrieval-Augmented Generation) lets LLMs access your current data without retraining. Fine-tuning permanently updates the model with your data. RAG is faster to implement and keeps data private; fine-tuning gives better accuracy for specific domains. I often use both together.
How much does it cost to build an AI feature?▼
Costs depend on LLM API usage, data volume, and complexity. A simple chatbot might cost $100-500/month in API fees. Complex RAG systems with heavy usage might cost $1000-5000/month. I help optimize costs and implement caching strategies.
How do I keep my data private with AI?▼
I implement privacy-first architectures: use RAG with secure vector databases, open-source models on your infrastructure, or privacy-focused APIs. Your data never leaves your system unless you choose to use cloud LLMs with proper agreements.
Can AI integrate with my existing systems?▼
Absolutely. I can integrate AI into existing applications via APIs, webhook handlers, or middleware. Most implementations add AI as a new feature layer without disrupting current functionality.
How long does an AI project take?▼
Simple integrations (chatbot): 2-4 weeks. Complex RAG systems: 4-8 weeks. Fine-tuning: 2-6 weeks. Enterprise solutions with custom models: 2-3 months. I provide detailed timelines during discovery.
What about AI safety and hallucinations?▼
I implement safeguards: grounding responses in real data (RAG), prompt engineering, response validation, and human-in-the-loop for critical decisions. AI works best when combined with human oversight.
Ready to Build AI Into Your Product?
Let's explore how AI can solve your business problems and create new opportunities.
Start an AI Project