Post-Purchase CX Automation Platform built for Retail. WeSupply helps retailers reduce their customer support tickets with Pre-purchase delivery prediction, automated shipping tracking, proactive notifications and self-service returns.
Orders can be pushed into the WeSupply platform via a JSON containing the order data. See the docs here
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
The WeSupply API serves as a bridge connecting your ecommerce platforms to WeSupply's order tracking and logistics features. By utilizing the API within Pipedream, you unlock the potential to automate order updates, streamline returns, and enhance customer service interactions. Pipedream's serverless platform allows you to create workflows that trigger based on certain events, process data, and connect to countless other APIs and services—essentially turning your WeSupply data into actionable insights and automated tasks without the need for a dedicated backend infrastructure.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
wesupply: {
type: "app",
app: "wesupply",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://${this.wesupply.$auth.client_name}.labs.wesupply.xyz/api/orders/lookup`,
headers: {
Authorization: `Bearer ${this.wesupply.$auth.oauth_access_token}`,
},
})
},
})
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}