All Case Studies

E-Commerce · AI Agent · UK

400–600 Emails a Day.
62% Now Fully Automated.

A UK luxury e-commerce brand was drowning in routine support tickets across three disconnected systems. We built a LangGraph agent that reads, decides, and resolves — or routes to a human with full context.

Industry E-Commerce (Luxury)

Location United Kingdom

Team 35 employees

Timeline 4 weeks

62%

tickets fully automated
zero human touch

<2min

auto-resolution time
~~8 min manual~~

<4hrs

peak backlog cleared
~~36–48 hrs~~

🛍️

Shopify

Orders · Shipping · Tracking

💳

Stripe

Payments · Refunds · Disputes

🗄️

Support DB

Tickets · SLA · VIP Status

The Problem

8 Minutes. 6 System Checks.
Repeated 600× a Day.

0:00

Read Email

Parse intent

1:30

Support DB

History + SLA

3:00

Shopify

Order + tracking

4:30

Stripe

Payment status

6:00

Draft Reply

Write response

7:30

Log + Send

Close ticket

Three Systems — No Shared Context

🛍️

Shopify

✕

💳

Stripe

✕

🗄️

Support DB

36–48h

peak backlog during product drops & holidays

Next day

international response across all time zones

70%

tickets required zero judgment — pure data retrieval

Agent Architecture

LangGraph State Machine.
5 Nodes. 3 Systems.

Every ticket flows through a deterministic pipeline with confidence-gated routing.

📧

INCOMING EMAIL

IMAP ingestion → Redis queue → agent triggered

NODE 01

🏷️ Classify

Intent + entity extraction, language detection, priority scoring

NODE 02

🔍 Retrieve Context — parallel tool calls

All 3 systems queried simultaneously · graceful degradation if one unreachable

NODE 03

⚖️ Decide — confidence-gated routing

Routing decision made · human-in-loop checkpoint if below threshold

✅ NODE 04A — ACT

Draft reply in detected language · initiate Stripe refund if needed · update Shopify order note · log interaction · send

🔔 NODE 04B — ESCALATE

Create enriched ticket with all context + issue summary + suggested resolution — human starts from full picture

Full Stack

LangGraph

Orchestration

GPT-4o

Reasoning

Redis

Queue + Cache

Python

Runtime

Shopify API

Orders

Stripe API

Payments

PostgreSQL

Support DB

IMAP

Ingestion

Technical Proof

"Where's My Order?"
Resolved in 94 Seconds.

● LIVE AGENT TRACE

Real ticket. Zero human involved. Full trace below.

📧 INCOMING EMAIL

from: sarah@example.com
subject: Where is my order #38291?
body: Hi, I placed an order 5 days ago and haven't received any shipping confirmation. Can you check?

🏷️ NODE 01 — CLASSIFY +1.2s

intent: order_status_inquiry confidence: 0.97
entities: order_id: #38291 language: en priority: medium

🔍 NODE 02 — RETRIEVE CONTEXT (PARALLEL) +3.8s

── shopify ────────────────────── 820ms
order: #38291 · placed 5 days ago · status: shipped
tracking: RM2948571GB · Royal Mail · last scan: local depot

── stripe ─────────────────────── 640ms
payment: £189.00 · status: succeeded · no disputes

── support_db ──────────────────── 310ms
customer: tier: gold · 12 prior orders · 0 open tickets

⚖️ NODE 03 — DECIDE +1.4s

verdict: AUTO-RESOLVE confidence: 0.94
reason: order shipped, tracking available, payment clear, gold customer

✅ NODE 04A — ACT +1.8s

actions:
1. Generated personalized reply with tracking link + estimated delivery
2. Added Shopify order note: "auto-replied re: shipping status"
3. Logged interaction in support DB · ticket auto-closed
4. Email queued for send

Impact

Same Volume. Same Team.
60% Less Manual Work.

62%

tickets fully resolved
without human touch

<2m

auto-resolution time
~~8 min manual~~

<4h

peak backlog cleared
~~36–48 hrs~~

Before → After

Resolution Time Per Ticket

Before (manual)

8 min

After (auto)

~2 min

Peak Period Backlog

Before

36–48 hrs

After

<4h

Tickets Automated

Before

After

62%

Abstrabit Technologies AI Process Automation