Project avara · HTML Copy Avara Labs – Case Study | Krish Shah
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E-commerce support automation interface
E-commerce AIEmail AutomationNLPMulti-Brand

Avara Labs

AI-Powered E-commerce Email Automation Platform

IndustryE-commerce / Support
My RoleSenior AI Engineer
Automation Rate~80% of queries
IntegrationsShopify, WooCommerce, Magento
ComplianceGDPR by design
01 — Overview

Project Overview

Avara Labs is a modular email automation platform built for e-commerce brands handling high volumes of customer support. It classifies incoming emails by intent, retrieves the relevant SOP and brand guidelines, then generates and sends accurate, brand-appropriate responses — automatically for ~80% of queries, with human review for the rest.

The platform supports multiple brands simultaneously, each with independent tone profiles, SOP documents, and escalation rules — all configurable without engineering changes.

02 — Problem Statement

The Challenge

Growing e-commerce brands were drowning in support email volume. Manual responses were slow, inconsistent in tone, and didn't scale — support teams were backlogged, customer satisfaction was dropping, and there was no clear path to handle the next wave of volume growth.

  • Response times measured in hours or days during peak periods
  • Brand voice inconsistencies across different agents and shifts
  • Support teams spending 80%+ of time on repetitive, low-complexity queries
  • No system to enforce brand-specific SOPs or escalation policies at scale
  • Multiple e-commerce platforms (Shopify, WooCommerce, Magento) with no unified data layer
03 — Solution

What We Built

A pipeline-based automation platform with five core stages: email ingestion and classification, SOP retrieval, dynamic response generation with brand tone enforcement, optional human review, and delivery. Each stage is independently configurable per brand client.

A human-in-the-loop dashboard gives support agents visibility and override control for any email — complex queries, escalations, or cases where confidence falls below threshold are routed to agents immediately rather than generating a potentially incorrect automated response.

04 — Architecture

System Architecture

Platform Connectors

Shopify, WooCommerce, and Magento API integrations for order, return, and customer data

Intent Classifier

Transformer-based model detecting email intent, urgency, and required resolution type

Document Intelligence

SOP-aware retrieval layer surfacing relevant brand policies for each email category

Templating Engine

Dynamic response generation with per-brand tone controls and live variable injection

Human-in-the-Loop

Review dashboard for agent override, confidence-threshold escalation routing

Async Task Queue

Celery + Redis for high-throughput async processing without blocking the main pipeline

05 — Tech Stack

Technologies Used

Python FastAPI Transformer-based Intent Detection Generative Response Layer Shopify API WooCommerce API Magento API PostgreSQL Redis Celery
06 — Impact & Results

Outcomes

80%Queries automated end-to-end
↓ hrsAverage response time reduced
MultiBrands supported simultaneously
GDPRCompliant by design

Support teams were freed from repetitive queries and redirected to complex customer situations requiring genuine human judgment. Brands saw meaningful improvements in response time, consistency, and customer satisfaction — while handling more volume with the same headcount.

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