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Projects give you a way to organize your trained models and manage their deployments. You create a project, deploy a fine-tuned model to it, and then run inference against that project’s endpoint. Each project maintains a deployment history so you can track which model version is live and roll back if needed.
After deploying a model, use POST /projects/:project_id/inference to run predictions through the project endpoint — no need to track training job IDs in your application code.

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List projects

GET /projects Returns all projects in your account. 
curl https://api.pioneer.ai/projects \
  -H "X-API-Key: YOUR_API_KEY"
Response
​
projects
object[]
Array of project objects.

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Create a project

POST /projects Creates a new project. Request body
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name
string
required
A unique, human-readable name for the project.
curl -X POST https://api.pioneer.ai/projects \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "production-ner"
  }'
Response
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id
string
The new project’s unique ID. Use this in all subsequent project and deployment requests.
​
name
string
The project name.

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Delete a project

DELETE /projects/:project_id Permanently deletes a project and its deployment history. Any deployed models associated with the project will no longer be accessible through the project inference endpoint.
Deleting a project removes the deployment endpoint immediately. Make sure any clients using the project inference endpoint are updated before deleting.
Path parameters
​
project_id
string
required
The project ID.
curl -X DELETE https://api.pioneer.ai/projects/YOUR_PROJECT_ID \
  -H "X-API-Key: YOUR_API_KEY"
Returns 204 No Content on success.

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Deploy a model to a project

POST /projects/:project_id/deployments Deploys a trained model to a project endpoint, making it available for inference. Creating a new deployment replaces the currently active model for that project. Path parameters
​
project_id
string
required
The project ID to deploy to.
Request body
​
training_job_id
string
required
The ID of the completed training job whose model you want to deploy. The training job must be in complete status.
curl -X POST https://api.pioneer.ai/projects/YOUR_PROJECT_ID/deployments \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "training_job_id": "YOUR_TRAINING_JOB_ID"
  }'
Response
​
deployment_id
string
Unique ID for this deployment.
​
status
string
Current deployment status. Values: deploying, active, failed.
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training_job_id
string
The training job ID that was deployed.

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List deployment history

GET /projects/:project_id/deployments Returns the full deployment history for a project, ordered from most recent to oldest. Use this to track which model versions have been deployed and when. Path parameters
​
project_id
string
required
The project ID.
curl https://api.pioneer.ai/projects/YOUR_PROJECT_ID/deployments \
  -H "X-API-Key: YOUR_API_KEY"

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Run inference on a project model

POST /projects/:project_id/inference Runs a prediction against the currently active model deployed to this project. This endpoint abstracts away the underlying training job ID so your application only needs to reference the project. Path parameters
​
project_id
string
required
The project ID.
Request body
​
text
string
required
The input text to run predictions on.
​
schema
object
The output schema for encoder models. Accepts optional keys: entities (list of entity type strings), classifications (list of {task, labels} objects), structures, and relations. For decoder models, use "task": "generate" instead.
curl -X POST https://api.pioneer.ai/projects/YOUR_PROJECT_ID/inference \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Apple CEO Tim Cook announced the new MacBook at WWDC.",
    "schema": {
      "entities": ["person", "organization", "product", "event"]
    }
  }'