I build custom AI agents that connect large language models to your actual tools, data, and workflows — not chatbots that just talk, but systems that look things up, take actions, and complete multi-step tasks. Every agent is built for production: grounded in your own data to reduce hallucinations, rate-limited, and tested against real questions before launch.
What I Build
- Custom AI agents with function calling and tool use — looking up records, updating databases, sending messages
- RAG-based chatbots trained on your own documents and knowledge base
- Multi-agent systems that split research, writing, and fact-checking across specialized agents
- AI agents embedded in Telegram and Discord bots
- Internal AI assistants for team knowledge search
- AI sales and support assistants that qualify leads or automate ticket responses
Real Use Cases
- Qualifying inbound leads automatically before a human ever gets involved
- Automating the repetitive third of a support queue — order status, account basics, documented FAQs
- Searching scattered internal docs and wikis so employees stop hunting for answers that already exist
- Extracting structured data from invoices, contracts, and forms instead of manual re-typing
Technology
- OpenAI, Anthropic Claude, and open-source LLMs, chosen based on cost, latency, and data requirements
- Vector databases for grounding responses in your real data (RAG)
- Node.js and Python backends with function-calling / tool-use APIs
- Telegram Bot API and Discord.js for chat-native agents
Built for Production, Not Just a Demo
A working demo and a production-ready agent are different things. I ground answers in retrieved content instead of the model’s general training data, add rate limiting and clear guardrails to the system prompt, and stream responses so the interface feels responsive rather than sluggish. Before launch, I test against a real set of questions your users are likely to ask, including edge cases with no good answer, so failures show up before your customers find them.
FAQ
How long does it take to build a custom AI agent?
It depends on scope. A single-tool agent with one function call and one data source can ship in days. Multi-agent systems with several integrations take longer. I scope this with you before starting so there are no surprises.
Do you train a custom model?
Usually not. Most agents work better with retrieval-augmented generation and prompt engineering over your own data, layered on top of an existing model, rather than a full fine-tune. A fine-tune makes sense in narrower cases — happy to talk through which applies to you.
What happens if the agent gives a wrong answer?
No technique eliminates hallucinations completely. I reduce them by grounding responses in your real content, adding guardrails to the system prompt, and testing against real questions before launch to catch obvious failure cases early.
Related Reading
- What Is an AI Agent? A Practical Definition for Business Owners
- How Much Does It Cost to Build a Custom AI Agent in 2026?
- Function Calling and Tool Use: How AI Agents Actually Take Actions
- Multi-Agent AI Systems: When One AI Isn’t Enough
- RAG vs Fine-Tuning: How to Actually Train an AI Chatbot on Your Data
Discuss Your Project
Have a specific AI agent in mind? Get in touch to talk through scope, or reach out via Fiverr if you’d rather start there.