EliconStart a project →
← ALL WORK

CASE 04

10 weeks

LLM Support Triage for a Retail Chain

Cut first-response time and auto-resolved a meaningful share of tickets.

  • Python
  • FastAPI
  • OpenAI
  • pgvector
  • Redis
  • Next.js

THE PROBLEM

Support volume outgrew the team. First-response time regularly exceeded a day, and routing to the right agent was handled manually by a lead who had become a bottleneck.

WHAT WE DID

  1. 01

    Built a triage classifier fine-tuned on twelve months of anonymized tickets.

  2. 02

    Stood up a RAG layer over the product catalog and policy docs so agents (and the model) pull exact passages with citations.

  3. 03

    Designed an escalation router: confident auto-responses ship, edge cases route to the right human with context pre-drafted.

  4. 04

    Shipped an agent console in Next.js with live queues, SLA timers, and a feedback loop that retrains the classifier weekly.

OUTCOME

First-response time dropped by roughly 60%. A meaningful portion of tickets now resolve without a human touch, and the lead is back to doing lead work.

HAVE A PROJECT LIKE THIS?

Let’s talk about yours.

Start a project →