7-Eleven Conversational AI Chatbot
Built the chat commerce experience inside Thailand's 7-Eleven app, serving 16M+ monthly active users across 15,000+ stores nationwide.
16M+
monthly active users
15k+
stores nationwide
3
distribution channels
+15%
customer satisfaction
context
7-Eleven Thailand isn't like 7-Eleven anywhere else. With over 15,000 branches blanketing the entire country, it's less of a convenience store chain and more of a national infrastructure. The mobile app had 16 million+ monthly active users, and the company was looking to unlock new digital sales channels as part of a larger digital transformation initiative.
The goal was to build a conversational layer that could serve across 3 distribution channels: answering customer inquiries, driving product sales through chat, and supporting the app's real-time delivery service. All powered by NLP, before the LLM era.
This was my first PM role. I joined as a junior product manager at Sertis (a data and AI consulting firm) and owned this project end-to-end.
๐ช7-Eleven in Thailand is amazing. We have over 15,000 branches. Seriously, look it up!
what we built
A conversational AI chatbot embedded in the 7-Eleven mobile app, powered by NLP using Google Dialogflow for intent recognition and RegEx-based pattern matching for structured flows. The chatbot served two distinct roles:
customer support agent
Handled general inquiries like store locations, operating hours, promotions, and order status for 7-Delivery (the app's real-time delivery feature).
chat commerce assistant
The more ambitious part. Users could browse products, see real-time stock at nearby stores, get personalized recommendations, and complete purchases, all without leaving the chat.
chat commerce flow
Detect user location
Map the user to their closest 7-Eleven stores automatically.
Check real-time stock
Query live inventory across 15,000+ stores to show what's actually available.
Surface products in chat
Display product carousels with promotions and discounts applied automatically.
Personalized recommendations
Suggest products based on purchase history and user profile, making each conversation feel relevant.
Complete purchase in chat
The entire shopping flow, from discovery to checkout, happened inside the conversation. No app switching needed.
what i actually did
requirements & PRD
Gathered requirements from the 7-Eleven business team and translated them into detailed product requirements documents, defining user flows, edge cases, fallback behaviors, and success metrics.
conversational design
Designed the conversational flows, mapping out intents, utterances, entities, and response trees. This is where the real product thinking lived: how should the bot respond when it doesn't understand? When should it escalate to a human? How do you make a carousel of snacks feel natural inside a chat thread?
engineering coordination
Ran the engineering team at Sertis while integrating closely with 7-Eleven's internal dev team. Two separate engineering orgs building one product, so alignment was constant work.
testing & iteration
Ran testing cycles and continuously gathered feedback from users to refine conversational flows, improve intent recognition accuracy, and reduce fallback rates.
what made this interesting
scale
16 million monthly active users across a nationwide app. Even small improvements in conversion or deflection rates translated to significant business impact.
chat commerce was still new
This was before the ChatGPT era. Building a commerce experience inside a chat interface required creative conversational design without the benefit of large language models. Every flow was hand-crafted.
real-time inventory integration
The chatbot checked live stock across 15,000+ stores, matched products to the user's nearest location, and applied dynamic promotions. The technical integration was non-trivial.
personalization at scale
Product recommendations were tailored to individual users based on purchase history and profile data, making each conversation feel relevant rather than generic.
why this matters
This was my entry point into AI product management, and it shaped how I think about building AI products to this day. Working with NLP before the LLM era taught me that good conversational design isn't about the model, it's about understanding what users actually need at each moment in a conversation.
The patterns I learned here, intent mapping, fallback design, human escalation logic, conversational UX, carried directly into the RAG Copilot I built years later.
chat commerce before the LLM era.
every flow hand-crafted, every intent mapped, serving 16M+ users across 15,000+ stores in Thailand's largest retail app.