I will build a secure self hosted open webui and chatwoot ai panel


About this gig
Unify Your Business AI and Customer Communications Under One Self-Hosted Roof.
Stop paying predatory per-user monthly SaaS fees or seat licenses just to give your team access to modern AI interfaces and customer service desks. I will deploy a powerful, production-ready, open-source workspace using Open WebUI and Chatwoot.
This centralized system creates a secure firewall around your organization's communications. Your internal team gets an enterprise-grade chat console to interact with self-hosted or API-driven AI models, while your support staff gains an omnichannel inbox to manage live customer conversationsall from a single, unified, open-source dashboard that you run and own entirely.
What I Do For Your Organization:
- Deploy complete Docker-based Open WebUI and Chatwoot infrastructure.
- Connect your local or cloud models (via LiteLLM, Ollama, OpenRouter, or OpenAI).
- Unify communication pipelines (Website Live Chat, Email, or Webhooks) into one inbox.
- Configure strict multi-user access rules to keep your internal data private.
- Deliver clean architectural knowledge-base handoffs.
Take back your data sovereignty and eliminate recurring software overhead today.
Get to know Henry Weismann
Enterprise Grade AI Infrastructure Architect, Open Source, Self Hosted Solutions
- FromUnited States
- Member sinceSep 2013
- Avg. response time2 hours
- Last delivery2 years
Languages
English
FAQ
Do I have to pay ongoing fees per team member or support seat?
No. Because this entire communication stack is open-source and self-hosted on your private server infrastructure, you escape all monthly subscription scaling costs.
Can we keep internal employee AI testing separate from live customer chats?
Yes. The system separates the internal Open WebUI console workspace from public-facing customer communication tabs to ensure zero accidental private context leakage.
What infrastructure do I need to host this stack?
Any modern VPS (Ubuntu recommended) or cloud-compute engine capable of running Docker containers. A minimum of 4GB RAM is recommended, scaling up depending on traffic or if you run local models.

