Automating Thousands of Daily Customer Conversations Across Five Channels

Consumer brands operating across social media, messaging apps, and web chat face a support scaling problem. Each channel generates its own stream of inquiries that human teams cannot handle cost effectively at volume. Red Buffer built an AI assistant that automates conversations, accesses real-time order data, and hands off to humans when needed.

Outcome

A production-ready AI assistant that handles thousands of daily conversations across Instagram, Messenger, Viber, and web chat with real-time order data access, persistent memory, vision support, and seamless escalation to human agents.

Thousands of Daily Conversations Automated

Routine customer inquiries handled automatically without human intervention.

Instant Response Times

Real-time updates on orders, deliveries, and branch information delivered directly in conversation.

Improved Customer Satisfaction

Persistent memory and image understanding enabled personalized and context-aware support.

Scalable Cloud-Native Performance

AWS Fargate enabled reliable operation during peak demand without manual scaling.

ROLE

Conversational AI design using LangGraph orchestration, multi-channel integration, real-time API integration, vector search implementation, vision capability, and human agent escalation.

TOOL

LangGraph, OpenAI, Qdrant, Chatwoot, MongoDB, AWS Fargate

DURATION

Single-phase build with production deployment and ongoing optimization.

Our Approach

  1. Multi-Turn Conversational Orchestration

    Designed structured conversational flows using LangGraph to support context-aware multi-turn dialogues that feel natural rather than the rigid decision tree experience typical chatbots provide.

  2. Multi-Channel Unified Support

    Connected the assistant to Instagram, Facebook Messenger, Viber, and the website through Chatwoot creating a single support layer instead of separate bots for each channel.

  3. Real-Time Data and Persistent Memory

    Integrated internal APIs for live order, delivery, and branch data. MongoDB-based session memory retains context across interactions so customers do not repeat information between conversations.

  4. Vision, Semantic FAQ Retrieval, and Human Handoff

    Qdrant vector search retrieves FAQs by intent rather than keywords. The assistant processes uploaded images such as order screenshots and receipts. When automation limits are reached, Chatwoot transfers conversations to human agents with full context preserved.

Why It Matters

This architecture combining conversational orchestration, multi-channel integration, real-time data access, vector search, and human handoff applies to any high-volume consumer support operation across retail, hospitality, logistics, and e-commerce where scaling human teams linearly with growth is not sustainable.

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