WHMCS AI Helpdesk Assistant Module
WHMCS AI Helpdesk Assistant Module
What it does
WHMCS AI Helpdesk Assistant Module Deliver smarter, faster customer support with the WHMCS AI Helpdesk Assistant. This powerful module adds an intelligent, real-time chatbot to your website, instantly responding to client queries using your WHMCS knowledgebase, FAQ’s and internal data. It operates 24/7, reduces ticket volume, and improves customer satisfaction.
Does the same job
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- HGHelpHub – GPT chatbot for any site2023 · helphub.commandbar.com · ▲108
Hi HN, I’m the founder of a SaaS platform called CommandBar (YC S20). We’ve been mucking around with AI-related side quests for a while, but recently got excited enough about one to test it with some customers. Results were surprisingly good so we decided to launch it. HelpHub is AI chat + semantic search for any website or web app. You can add source content in 3 ways: -Crawling any public site via a URL (e.g. your marketing site or blog) -Syncing with a CMS (like Zendesk or Intercom) -Add content manually The chatbot is then “trained” on that content and will answer question’s based on…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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