What it does
LiveAssist AI runs four specialized agents in parallel while users describe their problem. The Retrieval Agent fetches knowledge base articles in under 50ms. The Context Agent extracts order IDs and displays live tracking widgets. The Sentiment Agent detects frustration keywords and escalates priority. The Insights Agent classifies intent and auto-selects the correct category.
The result: users typing "I want to return my ORD-12345" immediately see the refund policy article, a working "Start Return" button, and an order status widget. Many never submit the ticket.
The architecture trade-offs
The project indexed six support articles with a custom smartAction field that transforms search results into actionable UI components. Query construction concatenates the subject and description fields to capture full context. Multi-attribute matching searches across titles, content, and tags simultaneously.
The 300ms debounce is the interesting bit. Too short and you're burning API calls on incomplete thoughts. Too long and the system feels laggy. The developer targeted the 100ms perceived-instant threshold: Algolia returns results in 10-50ms, leaving 50ms for rendering.
Why this pattern matters
Traditional support is linear: user submits, agent searches, agent replies. This flips it: continuous retrieval happens during problem description. The developer claims 60% ticket deflection, though that's based on the hackathon demo, not production data.
The broader question: are enterprises ready for proactive support automation? The tech works. The business case is obvious: reduce volume, improve satisfaction, lower costs. The barrier is usually organizational: support teams hesitant to trust algorithmic routing, legal departments nervous about automated advice.
What's missing
The demo runs on six articles. Production knowledge bases have thousands, with inconsistent formatting, outdated content, and coverage gaps. The system needs graceful degradation when it can't find answers. The sentiment detection uses keyword matching, which misses sarcasm and context. The auto-routing assumes clean category boundaries.
Still, the core insight holds: if search is fast enough to feel telepathic, you can fundamentally change how support works. Whether enterprises actually implement this pattern depends less on the technology and more on whether they're willing to rethink the support org chart.
The code is public on GitHub. The live demo is at live-assist.netlify.app.