Custom GPTs

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Overview

This approach lets you build a Custom GPT that can call Spark API endpoints as “Actions”, so users can ask questions in natural language and your GPT can fetch Spark insights/audience outputs in the background. This is useful when:

• You want a repeatable workflow (e.g., “focus group mode”, “audience profiler mode”).
• Add additional guardrails to ensure non-expert users get the correct data
• You want Spark data available inside a dedicated GPT rather than only in the main ChatGPT + connector experience.

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Prerequisites

You will need:

• A GWI API token with access to Spark API
• A ChatGPT plan that supports Custom GPTs with Actions

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How to use

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Create your Custom GPT

1. In ChatGPT, go to Explore GPTs
2. Click Create
3. Give it a name (e.g., “GWI Spark Focus Group – UK Gen Z Travel”)

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Add Spark as an Action

1. Go to the GPT Configure tab
2. Scroll to Actions
3. Click Add action
4. Choose Import from URL or paste a schema manually
   
   Recommended OpenAPI schema (copy/paste)
   Copy

   Ask AI

   openapi: 3.1.0
   info:
     title: GWI Spark API
     description: Chat with the GWI Spark service to retrieve audience insights.
     version: 1.0.0
   
   servers:
     - url: https://api.globalwebindex.com
   
   paths:
     /v1/spark-api/generic:
       post:
         operationId: sparkChat
         summary: Chat with Spark
         description: Send a prompt to Spark and receive insights.
         security:
           - ApiKeyAuth: []
         requestBody:
           required: true
           content:
             application/json:
               schema:
                 $ref: '#/components/schemas/SparkRequest'
         responses:
           "200":
             description: Successful response
             content:
               application/json:
                 schema:
                   $ref: '#/components/schemas/SparkResponse'
   
   components:
     schemas:
       SparkRequest:
         type: object
         required:
           - prompt
         properties:
           prompt:
             type: string
             description: The question or instruction sent to Spark
           chat_id:
             type: string
             description: Existing Spark chat session ID (optional)
           docked_audiences:
             type: array
             items:
               type: string
             description: Optional list of audience UUIDs
   
       SparkResponse:
         type: object
         properties:
           message:
             type: string
           chat_id:
             type: string
           insights:
             type: array
             items:
               type: object
               properties:
                 id:
                   type: string
                 text:
                   type: string
           sources:
             type: object
   
     securitySchemes:
       ApiKeyAuth:
         type: apiKey
         in: header
         name: Authorization
   

• What it should look like once copied in

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Configure Authentication (API token)

In the Action auth settings:

• Choose API Key
• Location: Header
• Header name: Authorization
• Value: your token (stored by ChatGPT as the secret)

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Add instructions so the GPT uses Spark correctly

In your GPT’s Instructions, add guidance like:

• When to call Spark (e.g., whenever user asks for data-backed insights)
• Ask for missing parameters (dataset, country, audience definition)
• Keep responses grounded: cite what the API returned; don’t invent stats

Example instruction snippet you can paste:

Spark usage: Use Spark to answer specific audience questions (who they are, what they do/like, attitudes, channels). If the user asks a big/strategic task (e.g., “make a media plan”), break it into a number of sub-questions (audience profile, interests, media habits, motivations/barriers, key messages, channel fit etc), run Spark for each, then synthesize into recommendations. One analytical goal per Spark call. Prefer multiple narrow calls over one broad call.

• Also add your use-case behaviour rules: Once you’ve told the GPT how to use Spark, don’t forget to add instructions for how you want it to behave overall—what extra context it should ask for and how it should structure its responses for your specific workflow.

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Testing and publishing

Once you’ve finished configuring the Custom GPT, test it using the chat panel on the right to make sure it behaves the way you intended (and is returning useful Spark-backed responses). When you’re happy with the outputs, click Create (top right) to publish it, then share it with the users you want to test and use it.

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Troubleshooting

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Import from URL doesn’t work

• Use Paste schema instead
• Or ensure the schema URL is publicly accessible and returns valid OpenAPI YAML/JSON

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401 Unauthorized

• The token is missing/invalid
• The token doesn’t have Spark access
• The header format is wrong (Authorization vs Bearer …)