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Fiverr Pro
English
$
USD
I will build an autonomous ai support agent and ticketing system
ZigShaw
About this gig
Stop losing high-value leads to basic, frustrating chatbots. I engineer custom autonomous AI support agents that actually resolve complex customer issues.
My systems go far beyond simple wrappers. I build strict LLM tool-calling architectures so your AI interacts directly with your database, fetches user records, tracks shipments, or handles automated ticketing without human intervention.
Why my architecture stands out:
- Stateful Memory: Custom database schemas (MongoDB/MariaDB) that maintain context over long conversations.
- Enterprise Fallbacks: Routes stuck sessions to human operators via live WebSockets.
- Zero Hallucinations: Strict prompt engineering and JSON-enforced outputs.
Test it live: Message me to get the link to my live Next.js production support console. You can test the exact architecture and the live human-takeover handoff yourself!
Note: Please message me before ordering to discuss your support bottlenecks and required API connections.
Get to know ZigShaw
ZigShaw
Backend Architecture and Autonomous AI Systems
- FromBangladesh
- Member sinceNov 2022
- Avg. response time1 hour
- Last delivery8 months
Languages
Bengali, English
I am a backend engineer and vibe coder specializing in custom AI agents and high-concurrency server architectures. I do not build generic AI wrappers. I architect stateful, production-ready systems using strict JSON tool-calling, robust database schemas (MariaDB/MongoDB), and live WebSockets. Beyond AI support structures, I have extensive experience handling complete Linux VPS migrations and deploying complex, fault-tolerant multiplayer environments (like RAGEMP) using TypeScript. Whether you need an autonomous enterprise support agent or a stable, custom backend, I write clean, scalable code.
My Portfolio
Other Chatbot Development Services I Offer
FAQ
Do you build basic ChatGPT wrappers?
No. I engineer custom backend architectures. My agents use strict JSON tool-calling to execute real business logic, query databases, and trigger external APIs autonomously.
What happens if the AI doesn't know the answer?
I build enterprise fallbacks. If the user gets frustrated or the AI hits a limitation, the system uses a live WebSocket protocol to instantly pause the AI and hand the chat over to a human operator.
What technologies do you use for the backend?
I build highly scalable, fault-tolerant environments using Node.js, TypeScript, OpenAI API, WebSockets, and robust databases like MongoDB or MariaDB.
