Streamlining Marketplace Operations With AI-Powered Chatbots
CLIENT
A large online marketplace operating across North America and Europe. The platform works with tens of thousands of sellers — from small brands to global retailers — and handles millions of customer interactions every month.
CHALLENGE
The first clear sign of the problem was communication overload. Support teams handled a constant flow of questions from customers checking on orders or returns, and from sellers asking about onboarding steps, listing rules, or payout timing.
Most of these inquiries were predictable, but the volume was high enough that it drew time away from cases where human judgment was actually needed. Even with internal macros and simple automations, agents still had to respond manually to recurring questions and switch between several backend systems to retrieve information.
It was clear the bottleneck was support capacity, not infrastructure. The goal became reducing unnecessary communication loops without changing how the marketplace operated.
SOLUTION
This wasn’t our first project with the client — we had previously helped with payments integration and catalogue consistency, so we understood the systems and workflows behind the marketplace.
When AI chatbots were first suggested, we didn’t assume they were the answer. The industry hype was strong, but our first question was whether a chatbot genuinely solved the client’s real pain points. To understand this, our product designer led a discovery phase that included:
interviews with customer support and seller-operations teams
calls with several high-volume sellers
a review of ticket categories across several months
Two things became obvious:
• customer and seller questions were different in content but very similar in structure
• users didn’t want a new tool — they wanted one place to get answers
From this, we recommended building two AI assistants under a single window, so users experienced one unified interface.
The separation into “customer logic” and “seller logic” existed only in the backend to match each workflow accurately. For end users, everything appeared in one chat panel — no juggling systems or switching channels.
1. Customer Logic Layer
Retrieved live order and shipping data
Answered questions about delivery, returns, and refunds
Checked refund eligibility
Resolved most issues without creating a ticket
2. Seller Logic Layer
Guided new sellers through onboarding and verification
Flagged listing inconsistencies in the catalogue
Explained policy updates in clear language
Drafted product titles or descriptions using metadata
Both operated behind the same interface. Users didn’t need to know which agent handled which task; the system determined that automatically.
RESULT
TECHNOLOGY STACK
How the system worked under the hood:
GPT-5 + Claude — combined reasoning layer. Claude processed long policies; GPT-5 generated structured conversational outputs.
Marketplace APIs — live access to order status, seller accounts, payouts, inventory, and shipping data.
Azure Cognitive Search — indexed all internal rules, helping the AI match answers with official references.
Logic Apps / Power Automate — executed steps like initiating refund checks or updating listing attributes.
SharePoint + Confluence Sync — kept all policies and guidelines version-aligned.
Azure OpenAI Service — hosted everything securely within the client’s tenant.
Azure Active Directory (RBAC) — controlled information access for buyer-facing vs. seller-facing logic.
Instead of focusing only on high-level metrics, the team evaluated the impact based on three concrete shifts observed during the first quarter:1. Fewer cross-team handoffs
Support teams noticed a clear drop in messages forwarded between departments. Customer support no longer had to ask seller operations to check catalogue rules, and seller operations didn’t need customer data to resolve order-related questions. The assistant handled most of this coordination independently.
2. Clearer workload patterns
With predictable work handled by the assistant, the support team could finally see what actually required human attention. This changed scheduling, staffing, and training priorities. The marketplace gained a more realistic view of its operational load instead of reacting to spikes blindly.
3. Earlier detection of underlying issues
Because the assistant processed large volumes of routine queries, recurring patterns surfaced quickly.
Examples:
A misconfigured shipping partner integration that caused repeated “Where is my order?” messages
A verification step in seller onboarding that confused new sellers every week
A recurring product-attribute mismatch triggered by one integration
These insights allowed the marketplace to fix root causes instead of treating symptoms. Overall, instead of constant firefighting, teams had more predictable workloads, fewer escalations, and clearer visibility into how processes behaved at scale.
Exploring AI tools for your marketplace or SaaS platform? Let’s look at what a system like this could deliver in your environment.
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