Snitch: The Multi-Agent Support Swarm
Automated customer support architecture for India's leading D2C fashion brand.
How we built a collaborative ecosystem of 5 specialized AI agents sharing memory to resolve 94% of Snitch's e-commerce customer support issues automatically.
94%
Auto-resolution rate
47s
Avg resolution time
4.8/5
CSAT score
83%
Manual support workload cut
Router Agent
Classifies messages and orchestrates the swarm
Product Expert
Handles sizing, materials, and product recommendations
Order Tracker
Connects to Shiprocket API for real-time delivery status
Billing Agent
Handles refunds, invoicing, and payment queries
Escalation Manager
Detects frustration and manages human handoffs
The Challenge
Support tickets do not follow a simple script.
Snitch, one of India's fastest-growing D2C men's fashion brands, was receiving over 800 support messages daily on WhatsApp. Their 6-person support team was permanently backlogged, causing response times to balloon to over 4 hours. Customers, frustrated by slow shipping updates and return delays, began canceling orders and leaving negative reviews.
Standard chatbots could not handle the complexity. A single support ticket often involved multiple intents: check shipping status, request a return code, complain about sizing, and demand a refund. Single-prompt AI bots would hallucinate, run out of system context, or fail to link up with Shiprocket and payment gateway systems.
The solution required an enterprise-grade multi-agent architecture: a team of specialized AI agents, each master of a single domain, working in concert to handle customer queries end-to-end.
Swarm simulation
Watch the agents communicate in real-time
See how a complex query involving shipping status, cancellation policies, and user sentiment is routed and resolved.
1. Intent Classification
The Router Agent extracts core actions, determines if APIs are required, and coordinates dependencies.
2. Collaborative Memory
Agents store context variables (such as verified order details) in a thread memory cache, keeping API calls fast.
3. Tone Control & Polish
The final response is formatted to feel natural and empathetic, removing all technical jargon or robot-speak.
Core Modules
Inside the support ecosystem
Dynamic Intent Router
First line of defense. Processes queries in 400ms, categorizes intents, and routes immediately to the specialized sub-agent with proper memory payloads.
Product Consultative AI
A consultative agent trained on materials, fit guidelines, and catalog data. Suggests items, matches sizes, and handles cross-selling naturally.
Courier API Integration
Direct connection with logistic partners. Tracks delays, updates dispatch locations, and generates shipping labels dynamically.
Invoicing & Refunds Handler
Cross-checks order IDs against transaction details. Prepares credit notes and processes refunds automatically through safe banking gateways.
Sentiment Escalation Manager
Scans for angry language, caps lock, or frustration tags. Automatically formats a human handoff ticket containing the full swarm chat summary.
Shared Memory Buffer
Allows seamless agent-to-agent transfers. If a query shifts from tracking to billing, the context is preserved so the customer never repeats details.
Architecture
Built for scalability
Results
Automation that drives sales
Within 14 days of deploying the Multi-Agent Support Swarm, Snitch saw ticket resolution rates soar. 94% of standard inquiries (tracking, sizing, billing) were resolved without human interaction. The average customer wait time crashed from 4 hours to just 47 seconds.
By automating the repetitive queue, Snitch was able to downscale their support staff from 6 active agents to a single manager overseeing the AI dashboard. CSAT scores increased to 4.8/5, and customer retention metrics improved by 18% in the first month.
94%
Resolution rate
47s
Resolution speed
4.8/5
CSAT score
83%
Savings in support costs
Drowning in support tickets?
Stop losing customers to slow reply times. Let us engineer a specialized support swarm that scales with your growth.