AI-Powered Customer
Support Platform
A full-stack support automation system combining AI-based intent classification with agentic workflows — built on Groq's Llama 3.3 with streaming completions. Auto-resolves 72% of tier-1 tickets without human intervention, and extends to scheduling appointments, booking support calls, and guiding users through multi-step service flows.
100% of tier-1 support was manual — slow, inconsistent, and unscalable.
Every customer ticket required a human agent to read, classify, and respond. As ticket volume grew, this created bottlenecks, inconsistent response quality, and an agent team under constant pressure. Tier-1 queries — password resets, status checks, standard troubleshooting — consumed the same human time as genuinely complex escalations.
The goal: reduce tier-1 manual load by auto-resolving high-confidence routine queries, extend to agentic workflows for scheduling and booking, and ensure the remaining 28% always gets a well-classified, context-rich handoff — not a blank-slate escalation.
Multi-layer LLM pipeline with autonomous resolution paths.
Three decisions that made the system production-ready.
The system is built on Groq's Llama 3.3 with streaming completions for low-latency responses — sub-second for most tier-1 queries. A unified gateway routes across providers by query type: Llama 3.3 handles the main classification and response generation; Claude routes for nuanced escalation decisions; Ollama serves deterministic FAQ lookups at near-zero cost. This removed single-provider lock-in and enabled per-route cost optimization without a monolithic dependency.
Every ticket is classified before any agent work begins. A lightweight classifier tags intent, confidence score, and entity extraction in <200ms. Only high-confidence tier-1 intents proceed to autonomous resolution — everything ambiguous routes to a human with the classification context pre-attached, reducing the agent's ramp-up time by ~60%.
Each conversation thread is a stateful session held in Redis with a 30-minute TTL. This lets any backend instance pick up any conversation without re-hydrating from the database, enabling horizontal scaling. The session schema is also the audit record — no separate write path, no consistency issues.