Prescreen Python API Docs | dltHub

Prescreening.io is an API-driven screening platform that provides risk scoring and screening checks (PEP, sanctions, AML, adverse media) for individuals and organizations. The REST API base URL is https://datafacade.prescreening.io/ and All requests require an OAuth2 access token..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Prescreen data in under 10 minutes.

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What data can I load from Prescreen?

Here are some of the endpoints you can load from Prescreen:

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How do I authenticate with the Prescreen API?

Authentication uses OAuth 2.0. Clients obtain an access token by calling the GetToken endpoint (POST) and include the token in the Authorization header as Bearer .

1. Get your credentials

1. Contact Prescreening/Zigram team to provision API access and receive ProjectAdminID / projectId and client credentials.
2. Use the provided credentials to call the GetToken endpoint (POST https://datafacade.prescreening.io/GetToken) to receive an access token and projectId in the response.
3. Use the access token in the Authorization: Bearer header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.prescreen_source]
access_token = "your_access_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Prescreen API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python prescreen_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline prescreen_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset prescreen_data
The duckdb destination used duckdb:/prescreen.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline prescreen_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads screening and download_pdf from the Prescreen API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def prescreen_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://datafacade.prescreening.io/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "screening", "endpoint": {"path": "api/Screening"}},
            {"name": "download_pdf", "endpoint": {"path": "DownloadPdfFile"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="prescreen_pipeline",
        destination="duckdb",
        dataset_name="prescreen_data",
    )
    load_info = pipeline.run(prescreen_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("prescreen_pipeline").dataset()
sessions_df = data.screening.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM prescreen_data.screening LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("prescreen_pipeline").dataset()
data.screening.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Prescreen data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 responses the access token is missing or expired. Re-obtain a token by calling GetToken and retry with Authorization: Bearer .

Bad request / invalid input

400 indicates invalid request parameters (for example, invalid Type — Type must be Individual, Organization, Vessels or All). Verify required fields (ProjectId, EntityName, Type, etc.) and formats before retrying.

Rate limits / too many requests

409 is documented as Too Many Requests. Implement exponential backoff and retry logic when receiving 409.

Server errors and timeouts

500/504 indicate server-side errors or gateway timeouts; retry with backoff and contact vendor support if persistent.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack