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Avara Labs – E-commerce Automation

Case Study

Avara Labs is an AI-powered email automation platform built for e-commerce brands handling high volumes of customer support. The system was designed to automate standard queries while preserving brand tone, SOP compliance, and human control.

  • Industry: E-commerce / Customer Support
  • Focus: AI-driven email automation
  • Outcome: ~80% of standard queries automated
  • Role: Senior AI Engineer (Architecture & Execution)

Problem

Small and mid-size e-commerce brands struggled to handle growing volumes of customer support emails. Manual responses were slow and inconsistent, leading to dissatisfied customers and overwhelmed support teams.

Context / Business Need

Avara Labs aimed to deliver a highly customisable email automation platform that integrated directly with each brand’s customer database and SOPs. The system needed to answer common queries automatically while allowing seamless manual intervention for complex cases.

Constraints

  • Multiple brands with distinct tone and SOPs
  • Integration with diverse e-commerce platforms and CRMs
  • High accuracy for standard queries (orders, returns, FAQs)
  • Strict customer data privacy requirements
  • Configurable AI behaviour per client

My Role

As Senior AI Engineer, I designed the document-intelligence layer, built the automation logic, and managed integrations with e-commerce APIs. I worked closely with brand stakeholders to capture tone, templates, and escalation rules.

System Design Approach

The solution was built as a modular system: structured data ingestion, intent classification, SOP-aware templating, and an AI response layer. Human-in-the-loop workflows allowed agents to review or override responses when needed.

MVP Design

The MVP automated the most frequent support queries—order status, shipping updates, and FAQs—using a hybrid of rule-based logic and generative AI to maintain consistency while preserving brand voice.

Architecture Breakdown

  • API connectors for e-commerce platforms and order systems
  • NLP-based email classification and intent detection
  • SOP-aware document intelligence layer
  • Dynamic templating engine with brand tone controls
  • Human-in-the-loop dashboard for review and escalation

Final Solution & Results

The platform automated up to 80% of incoming support queries, significantly reducing response times and increasing consistency. Support teams were freed to focus on complex customer issues, improving overall satisfaction.

Tech Stack

  • Python, FastAPI
  • Transformer-based intent detection
  • Rule-based + generative templating
  • Shopify, WooCommerce, Magento integrations
  • PostgreSQL, Redis, Celery