AI Customer Service Agent Demo

AI Customer Service Agent

Upload your docs → get a 24/7 support agent that answers from them, classifies every ticket, drafts replies for human approval, and escalates when it’s not confident.

Try the live demo →
Watch the 60-second Loom

How it works

1. Ingest your knowledge

Drag-and-drop PDFs, Markdown, and URLs. Chunks are embedded and indexed for semantic + keyword retrieval.

2. Chat with citations

An Intercom-style widget answers customer questions and surfaces the exact passages it used — no hallucinations.

3. Classify and route

Every ticket is auto-tagged (billing / bug / sales / escalation). Low-confidence answers drop into a human review queue.

60-second Loom

45-second flow: upload 3 legal docs → ask 2 questions → show the citation pop-out → open the escalation queue.


Free build spec

Get the full build spec PDF

One-page PDF with the architecture, the tech stack, the timeline (2–3 weeks), and the investment range ($9,990 – $25,000). Sent to your inbox in under a minute.


Who it’s for

Support leaders at SaaS, legal, and hospitality companies drowning in repeat questions who need to deflect 60%+ of tier-1 tickets without shipping wrong answers.

Tech under the hood

Next.js 15Claude Sonnet 4.6pgvectorPostgresTypeScriptTailwind CSS

Frequently asked questions

How does it avoid hallucinations?

Every answer is grounded in a retrieved passage from your uploaded documents, and the exact citation shows in a side panel. If confidence is below the threshold you set, the ticket goes to a human review queue instead.

What document formats are supported?

PDFs (scanned and text), Markdown, HTML, Notion and Confluence exports, Word docs, and plain URLs. Unstructured.io handles parsing; chunks are embedded with OpenAI or Voyage AI.

Does it replace Intercom or Zendesk?

No — it plugs in as the first-response agent. Approved replies post back to your existing help-desk via webhook; escalations route to human agents in their normal queue.

How is ticket classification trained?

Classes are defined by label + 3–5 example tickets each. Claude classifies via in-context learning. No fine-tuning required — accuracy typically hits 92%+ on day one.

Want one of these in your business?

We’ll scope, build, and hand off a production-ready system in 3–4 weeks.

Book a Strategy Call



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