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AI Customer Support Automation Platform
Built an AI-powered customer support platform using n8n, Ollama, Docker, MySQL, and REST APIs. The system automates customer inquiries, generates intelligent responses, and streamlines support workflows with a scalable, self-hosted AI architecture.
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Processing incoming PDF attachments and logging key email data manually takes hours of tedious, repetitive work. Every time a new invoice, quote, or report lands in your inbox, you have to manually open the email, download the PDF, read through dense pages to extract important details, format the data, and log it into Google Sheets row by row.
Which one is faster, saves more time, and prevents mistakes?
1 voted
25%
3 voted
75%
4 votes
Closed
The manual-vs-automated framing is clear, but I'm curious how the workflow copes with PDFs that don't share a layout, like scanned invoices versus quotes. Do you parse them with a fixed template or an extraction step, and what happens to rows it can't read confidently?
Resolve: an AI support agent that answers from a company’s own policies, looks up orders, and escalates weak matches to a human instead of guessing. A human approves replies before they go out.
Live demo: support-resolver.vercel.app
For the human approval step, I'd show the exact policy passage and its effective date beside the draft. Does Resolve flag a reply for re-review if the policy changes while it waits for approval?
Your Business Doesn't Need More Tools. It Needs Better Systems.
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When a CRM-to-database sync fails halfway, how do you handle it: idempotent retries, or a reconciliation pass afterward? That's usually where integrations start to drift.