TeamSupport Integration — Practical Implementation Guide

Introduction

This guide provides a narrative walkthrough for integrating TeamSupport with the RAIA agent. The primary goal of this integration is to synchronize support tickets, their associated actions, and wiki articles from your TeamSupport instance directly into the AI Agent's knowledge base. This connection provides the agent with a comprehensive view of your customer support interactions and internal knowledge, enabling it to support automated workflows and deliver more accurate, context-aware responses.

The Integration Process Overview

The integration is built upon a standard Extract, Transform, Load (ETL) framework. The process begins with the extraction of tickets, actions, and wiki articles from your TeamSupport instance using its API. This data is then transformed into a structured JSON format, a process that includes leveraging the Manus API to generate a derivative knowledge base for enriched content. Finally, the processed information is loaded into a PostgreSQL data lake hosted on Supabase. The RAIA agent accesses this data, and the entire process is automated to run monthly, ensuring the agent's knowledge remains consistently up-to-date.

Roles and Responsibilities

Two key roles are essential for a successful integration. The n8n Workflow Developer is responsible for configuring and deploying the n8n workflows that power the ETL process. The RAIA Agent Engineer provides support by assisting with the agent's configuration and ensuring the synchronized data is correctly ingested and utilized by the AI.For more technical details on the n8n-TeamSupport connection, you can refer to the native n8n integration documentation at https://n8n.io/integrations/teamsupport/.

Phase 1: Planning and Preparation

The initial phase, typically requiring 2-4 hours, is dedicated to planning the integration. The Workflow Developer will start by identifying your TeamSupport instance URL and generating the necessary API credentials. A critical step in this phase is to define the scope of the data to be synced, such as specific ticket types or wiki categories. The developer then maps the TeamSupport data fields—Tickets, Actions, and Wikis—to the target schema of the data lake. This planning stage concludes with the creation of an integration architecture diagram, a list of required APIs and credentials, and a risk mitigation plan.

Phase 2: Data Extraction and Transformation

This phase, estimated to take 4-6 hours, focuses on the technical implementation of the data extraction and transformation. The Workflow Developer will configure two separate workflows. The Fetch and Process TeamSupport Tickets sub-workflow is designed to retrieve tickets and their associated actions. Concurrently, the TeamSupport Wikis ETL workflow is configured to fetch wiki articles. In both workflows, data shaping logic is applied to convert the raw data into a structured JSON format, and the Manus API is used to generate a derivative knowledge base. The deliverables for this phase are the completed n8n workflows and samples of the transformed JSON data for both tickets and wikis.

Phase 3: Loading, Automation, and Synchronization

The final phase, requiring approximately 3-5 hours, involves loading the data and activating the automated synchronization. The Workflow Developer configures the main TeamSupport Ticket ETL Orchestrator workflow to manage the ticket data pipeline and verifies that the TeamSupport Wikis ETL workflow is correctly loading data. Both workflows use an "upsert" operation to efficiently load data into the PostgreSQL database. Once the data is loaded, the Raia API is triggered to process the new information. The process concludes by scheduling both workflows to run automatically each month. The final deliverables include the completed workflows, confirmation of a successful data load, and the configured monthly execution schedule.