AI Development Services
Looking for an AI developer? I build production-ready AI solutions with LangChain, RAG systems, and multi-agent architectures. 7+ years of full-stack experience, 15+ projects delivered.
What I Build
From simple AI integrations to complex multi-agent systems — I deliver solutions that work in production.
LangChain Development
Build sophisticated AI workflows and chains using LangChain. From simple chatbots to complex multi-step reasoning systems with memory, tools, and external integrations.
- Custom chain architectures
- Tool and function calling
- Conversation memory systems
- Streaming responses
RAG Systems
Implement Retrieval Augmented Generation systems that combine your proprietary data with LLM capabilities. Perfect for knowledge bases, documentation assistants, and enterprise search.
- Vector database integration
- Semantic search optimization
- Document chunking strategies
- Hybrid search (semantic + keyword)
Multi-Agent Systems
Design and build systems where multiple AI agents collaborate to solve complex problems. Navigator, Planner, Executor, Validator — each agent specialized for its role.
- Agent orchestration
- Inter-agent communication
- Self-validation loops
- Automatic fallback patterns
LLM Integrations
Integrate multiple LLM providers (OpenAI, Anthropic, Google, local models) with unified interfaces and automatic failover for production reliability.
- OpenAI GPT-4 / GPT-4o
- Anthropic Claude
- Google Gemini
- Local models (Ollama, LMStudio)
Technologies I Use
Modern AI stack for building scalable, production-ready solutions
AI Projects
Melio MealPlan AI
AI-Powered Meal Planning
**The agent.** AI meal-planning pipeline built as a single tool-using agent on a LangGraph StateGraph with a ReAct inner loop: Claude Haiku 3.5 calls a USDA lookup tool, but the submit schema has no nutrition fields, so the server computes every macro from ground truth — hallucinated calories are architecturally impossible. **Reliability & quality.** A 3-layer validate-and-retry cascade (programmatic checks → LLM-as-judge → blocking USDA fact-check) enforces macro targets and dietary restrictions, and typed MealPlanState is checkpointed to Postgres for crash-resume. **Dual RAG.** Two complementary RAGs split the work — a lexical USDA RAG (Postgres FTS + Haiku reranker) grounds every macro, while a separate semantic recipe RAG (HyDE → text-embedding-3-small → pgvector HNSW + RRF → Haiku reranker, seeded from RecipeNLG via LlamaIndex) feeds dish ideas into a retrieve_recipes node before generation. **Flywheel & eval.** Validated meals re-enter the corpus through a guarded data flywheel, an offline eval harness (RAGAS/DeepEval/promptfoo → MLflow, Recall@40, agent-trajectory metrics, CI gate) catches regressions, and every run is traced in self-hosted Langfuse.
GovChime Analytics Platform
Government Contracts Intelligence
**AI pipeline.** AI pipeline over government-contract data: LLM-powered data sanitization, contract-opportunity matching, and description generation, all with structured-output validation for consistent quality across 52M+ award rows. **Data trust.** Schema-drift guards (a >10% null-actionDate Slack alert) and expected-vs-actual completeness checks keep the upstream SAM.gov data trustworthy before it reaches the models. **Dev workflow.** Built with a full agentic development workflow — Claude Code, custom agents, MCP integrations, and TDD.
Formea AI Form Automation
Multi-Agent Chrome Extension for Intelligent Form Filling
Architected multi-agent system with 3 specialized agents (Planner, Navigator, Validator) orchestrated by an Executor with self-validation loops. LangChain integration supporting 8 LLM providers with automatic structured output fallback via Zod schemas. Built VBON Form Explorer agent for autonomous government form reverse-engineering using stable IDs and deterministic DOM diffing.
AI Development FAQ
What types of AI projects do you work on?
I specialize in production AI systems: chatbots with memory, RAG-powered knowledge bases, AI agents that can browse the web and fill forms, multi-agent systems for complex workflows, and LLM integrations into existing applications.
How long does it take to build an AI solution?
A simple chatbot with RAG can be production-ready in 2-4 weeks. More complex multi-agent systems typically take 6-12 weeks. I always start with a discovery phase to scope the project accurately.
Can you integrate AI into my existing application?
Yes, I regularly integrate AI capabilities into existing systems. Whether it's adding a conversational interface, implementing semantic search, or building an AI-powered feature — I can work with your existing tech stack.
What makes your AI solutions production-ready?
I focus on reliability: automatic fallbacks between LLM providers, comprehensive error handling, rate limiting, response validation, and proper logging. My solutions are built for real users, not just demos.
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