GPT-4o, Claude, and custom models
Seamless integration with leading language models. Not just API calls — optimized prompts, response parsing, error handling, and fallback strategies for production reliability.
Knowledge-augmented AI for your data
Build intelligent systems that reason over your documents. Vector databases, chunking strategies, embedding optimization, and hybrid search for accurate, context-aware responses.
Autonomous workflows with LangChain
Deploy AI agents that reason, plan, and execute tasks. Tool use, memory, multi-step reasoning — from simple automation to complex autonomous workflows.
Production-ready conversational AI UX
More than a chat box — streaming responses, markdown rendering, code highlighting, image support, conversation management, and accessibility.
Best-in-class AI infrastructure, selected for production reliability.
From AI strategy to production deployment — a process built for results.
Audit your data, define AI use cases, map user journeys, and establish success metrics. Deliverable: AI readiness assessment and roadmap.
Design the AI pipeline — model selection, data flow, RAG architecture, fallback strategies, and integration points with your existing systems.
Build iteratively with weekly demos. Focus on prompt engineering, accuracy tuning, and production concerns like latency and cost optimization.
Deploy with monitoring, gather real-world performance data, and continuously improve based on user feedback and AI output quality.
API-based AI (GPT-4o, Claude) uses pre-trained models via APIs — fast, cost-effective, excellent for most use cases. Custom models are trained on specific data — higher cost, longer development, but tailored to niche domains. I recommend starting with API-based AI and evolving to custom models only when needed.
A typical AI integration takes 4-8 weeks depending on complexity. Simple GPT-4o integration (chatbot, content generation) is 2-4 weeks. Advanced RAG pipelines with custom workflows are 6-8+ weeks. Enterprise AI architectures with multiple agents can take 3+ months.
RAG (Retrieval Augmented Generation) combines your data with AI models. Instead of relying only on training data, RAG fetches relevant information from your documents/database, then uses AI to generate accurate, context-aware responses. Essential for: customer support bots, document Q&A, product recommendations, and any case where AI needs to reference your specific data.
Hallucination mitigation is built into my architecture: context grounding with RAG, prompt engineering with guardrails, response validation layers, and human-in-the-loop fallback for uncertain answers. I also implement confidence scoring so the AI knows when to say 'I don't know' versus guessing.
API costs vary by provider: GPT-4o is ~$5-15/1M tokens, Claude is ~$3-15/1M tokens. For a typical SaaS app with 10K monthly users, expect $50-500/month in API costs. I optimize for efficiency — caching, batch processing, and model routing (cheaper models for simple tasks) to minimize costs.
Yes. Most AI integrations are backend/API work that doesn't require changing your frontend. I can add AI capabilities to existing Node.js, Python, React, or other stacks via REST APIs or SDKs. The exception is AI UX features (chat interfaces, streaming responses) which may need frontend changes.
Complete guide to adding GPT-4o to your React apps with streaming, context management, and production best practices.
Build production AI apps with LangChain: agents, chains, RAG pipelines, and deployment patterns.
Whether you need a chatbot, document search, or full AI automation — I can help you ship production-ready AI features in weeks, not months.