Hire me
Hire an AI agent and LLM developer
Production AI agents, RAG over your own data and LLM features inside your product, built with the backend, monitoring and tests they need to keep working.
Quick answer
I build AI agents, multi-agent systems and RAG pipelines with LangGraph, LangChain, LlamaIndex and n8n, on the OpenAI and Anthropic (Claude) APIs, and the Node.js or Python backend around them. I am currently building the AI product platform Joni (joni.ai), and I hold the Build AI Agents using LangGraph certification.
What I build
AI agents and multi-agent systems
Agents that plan, call your tools and APIs, and hand work to each other, coordinated with LangGraph.
RAG over your data
Chunking, embeddings and vector search, so answers are grounded in your documents and data.
LLM features in your product
Chat, summaries, extraction and AI assistants plugged into your existing backend.
Workflow automation
n8n workflows and agents that remove repetitive work between your tools.
Evaluation and monitoring
Test sets before release; tracing, quality, latency and cost tracking in production.
Cost and latency control
Caching, smaller models where quality allows, trimmed context and streaming.
Results on real products
Joni (joni.ai)
AI product platform
My main ongoing project at Quecko Inc.: I build the backend services, agent workflows and integrations that make the AI product work.
SnapText and Picture to Text
On-device AI
Text recognition that runs entirely on iPhone and Mac, so images never leave the device. Picture to Text reads 30+ languages.
Resume Builder – CV Maker AI
AI in a shipped app
An App Store app with an AI assistant that improves resume text and exports ATS-friendly PDFs.
Tools I use
LangGraphLangChainLlamaIndexn8nOpenAI APIAnthropic APIRAGVector searchPythonNode.jsTypeScriptAWSDocker
How a project works
- Scope call. We talk through the goal, the current state of the product and what success looks like.
- Written plan. You get fixed milestones, delivery dates and a clear price before any work starts.
- Build in small pieces. I ship in small steps with regular progress updates, so you always see what is built.
- Tested delivery. Delivery includes tests, documentation, handover and free support afterwards if something breaks.
Frequently asked questions
Which AI models do you work with?
I build on the OpenAI and Anthropic (Claude) APIs, with retries, timeouts, streaming, rate-limit handling and usage logging, and I choose the model per feature based on quality, speed and cost.
Can you add AI to our existing product?
Yes. I build API and LLM integrations that plug into your current backend, tools and data, so the AI feature works with what you already have.
Can the AI answer from our own documents?
Yes. That is what RAG is for: I index your content with embeddings and vector search, and the model answers from the retrieved context instead of guessing.
How do you keep LLM costs under control?
I cache repeated answers, use smaller models where quality allows, trim prompts and context, batch background work, and track cost and latency per feature.
How do you know the AI is good enough to ship?
I evaluate every change against test sets before release, then monitor answer quality, latency and cost in production.
Can you build an AI MVP from scratch?
Yes. I scope it with you, plan fixed milestones, and build the AI layer, the backend and the app, so you deal with one person.