Company Profile
Company Type
Direct-to-consumer brand
Team Size
30-150 employees
Industry
E-Commerce
Key Challenge
Struggling with inefficient manual chat support processes that were slowing growth and increasing operational costs. Their primary concern was order fulfillment speed.
Tools Connected
The Challenge
This direct-to-consumer brand had reached a breaking point with their manual chat support process. With 30-150 employees managing daily e-commerce operations, the team was spending an average of 25+ hours per week on repetitive chat support tasks that added no strategic value. The workload was unsustainable, and errors were becoming more frequent as volume grew.
The consequences extended beyond wasted time. In their e-commerce business, delayed chat support created a cascade of downstream problems — missed deadlines, frustrated stakeholders, and data quality issues that undermined decision-making. The team had tried hiring additional staff, but the cost was prohibitive and training new employees on their complex e-commerce processes took months. They needed a solution that could handle their current volume and scale with their growth, without requiring a proportional increase in headcount.
The Solution
The team selected Arahi AI to automate their e-commerce chat support workflow end-to-end. Implementation began with connecting their core tools — Shopify, Mailchimp, and Zendesk — to the Arahi AI platform. Using the no-code builder, they configured AI agents that replicate their best-performing team member's decision-making process, but at machine speed and consistency.
The AI agents handle every step of the chat support process: receiving incoming requests or triggers, analyzing the context using e-commerce-specific rules, making intelligent routing decisions, executing the core actions, and notifying the right stakeholders. What previously required 45+ minutes of manual work per instance now completes automatically in under 2 minutes. The agents also learn from corrections, continuously improving their accuracy. The team connected Google Analytics for tracking and reporting, giving leadership real-time visibility into chat support performance metrics for the first time.
The Results
Measurable improvements across key e-commerce chat support metrics.
Average Response Time
99% faster
Before
8 minutes
After
< 5 seconds
Queries Resolved by AI
New capability
Before
0%
After
72%
Customer Satisfaction
42% increase
Before
3.1/5
After
4.4/5
Support Cost per Interaction
86% savings
Before
$8.50
After
$1.20
After-Hours Coverage
Always on
Before
0% (business hours only)
After
100% 24/7
“We went from spending half our day on chat support to having it just happen automatically. The AI agents handle the routine work perfectly, and our e-commerce team can focus on the strategic decisions that actually move the needle. I wish we had done this a year ago.”
VP of Operations
Direct-to-consumer brand
Key Takeaways
The most important lessons from this e-commerce chat support automation project.
AI-powered chat support automation eliminated 88% of manual processing time for this e-commerce team, freeing staff to focus on high-value strategic work.
Implementation took less than a day — the no-code approach meant no IT bottleneck or months-long development cycle.
Error rates dropped by over 90%, significantly improving data quality and downstream decision-making.
The ROI was realized within the first month, with the solution paying for itself multiple times over through cost savings and productivity gains.
Implementation Timeline
From zero to production in 2 hours — here's how they did it.
Step 1: Connected e-commerce tools to Arahi AI
Integrated Shopify, Stripe, and ShipStation with Arahi AI using pre-built connectors — no API keys or custom code required. The team verified data flow between systems in under 15 minutes.
Step 2: Configured AI agent business rules
Defined the e-commerce-specific rules for chat support: scoring criteria, routing logic, escalation thresholds, and exception handling. The team used Arahi AI's visual rule builder to translate their existing process into automated workflows.
Step 3: Tested with live e-commerce data
Ran the AI agents on a week's worth of historical chat support data to validate accuracy and identify edge cases. Made minor adjustments to scoring weights and routing rules based on the results.
Step 4: Launched and monitored
Deployed the AI agents to production with the entire team notified via Google Analytics. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
2 hours
AI Agents
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating chat support in e-commerce.
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This case study represents a typical customer scenario. Individual results may vary.