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Challenge

Build a Vessel-Owner Legal Entity Evidence Briefing Tool

Develop a vessel-owner legal entity evidence briefing tool that joins synthetic vessel manifests with GLEIF entity data. Standardize fragmented maritime ownership formats using automated reconciliation to prevent incomplete global compliance profiles.

CybersecurityHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a vessel-owner legal entity evidence briefing tool that joins synthetic vessel manifests with GLEIF entity data. Standardize fragmented maritime ownership formats using automated reconciliation to prevent incomplete global compliance profiles.

Maritime compliance officer

Ingest synthetic vessel manifest records containing raw owner entity names and registration details.

Query a synthetic GLEIF API Golden Copy using fuzzy matching to reconcile the raw owner entity strings.

Extract canonical Legal Entity Identifiers (LEIs) and registration status for mapped maritime companies.

Output a vessel-owner legal entity evidence briefing combining the manifest data with verified LEI attributes.

Dashboard displaying reconciled vessel manifests, LEI matches, and alerts for orphaned maritime entities.

Generate synthetic vessel manifests in JSON format paired with mock GLEIF API responses containing standard LEI structures. Starter examples below are synthetic. They are not downloaded records.

GLEIF API References GLEIF API capabilities for searching and mapping unstructured entity strings to standardized LEI records.

mastra-ai, pydantic, httpx, pandas. Provides workflow orchestration to manage API polling, entity string matching, and report generation logic. General LLM for parsing fuzzy entity names and drafting the evidence briefing payload.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Successfully matches and retrieves canonical LEIs from the synthetic GLEIF API data.

Merges vessel manifest attributes seamlessly with standardized legal entity identifiers.

Generates an evidence briefing payload correctly tracking harmonization success and unmatched entity alerts.

```json [ { "input": { "manifest": { "vessel_name": "Sea Breeze", "mmsi": "123456789", "declared_owner": "Zephyr Holding Services Ltd" }, "gleif_api_response": { "data": [ { "attributes": { "lei": "213800XYZ123456789", "entity": { "legalName": { "name": "Zephyr Holding Services Limited" } } } } ] } }, "expected_output": { "record_type": "simplified synthetic app records, not complete official schema files", "vessel_name": "Sea Breeze", "harmonization_successful": true, "assigned_lei": "213800XYZ123456789", "canonical_name": "Zephyr Holding Services Limited" } }, { "input": { "manifest": { "vessel_name": "Nordic Trader", "mmsi": "987654321", "declared_owner": "Nordic Maritime AB" }, "gleif_api_response": { "data": [] } }, "expected_output": { "record_type": "simplified synthetic app records, not complete official schema files", "vessel_name": "Nordic Trader", "harmonization_successful": false, "assigned_lei": null, "canonical_name": null } } ] ```

Acts as a data enrichment layer; final determinations require human review of official registry documents. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Acts as a data enrichment layer; final determinations require human review of official registry documents. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Successfully matches and retrieves canonical LEIs from the synthetic GLEIF API data. Merges vessel manifest attributes seamlessly with standardized legal entity identifiers. Generates an evidence briefing payload correctly tracking harmonization success and unmatched entity alerts.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Ingest synthetic vessel manifest records containing raw owner entity names and registration details. Query a synthetic GLEIF API Golden Copy using fuzzy matching to reconcile the raw owner entity strings. Extract canonical Legal Entity Identifiers (LEIs) and registration status for mapped maritime companies. Output a vessel-owner legal entity evidence briefing combining the manifest data with verified LEI attributes.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Successfully matches and retrieves canonical LEIs from the synthetic GLEIF API data. Merges vessel manifest attributes seamlessly with standardized legal entity identifiers. Generates an evidence briefing payload correctly tracking harmonization success and unmatched entity alerts.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Dashboard displaying reconciled vessel manifests, LEI matches, and alerts for orphaned maritime entities.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Acts as a data enrichment layer; final determinations require human review of official registry documents.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Ingest synthetic vessel manifest records containing raw owner entity names and registration details.

  • Query a synthetic GLEIF API Golden Copy using fuzzy matching to reconcile the raw owner entity strings.

  • Extract canonical Legal Entity Identifiers (LEIs) and registration status for mapped maritime companies.

  • Output a vessel-owner legal entity evidence briefing combining the manifest data with verified LEI attributes.

Resources and assets

Reference links and supporting material

Dataset notes

Generate synthetic vessel manifests in JSON format paired with mock GLEIF API responses containing standard LEI structures. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Mastra AI

TypeScript agent framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest synthetic vessel manifest records containing raw owner entity names and registration details. Query...
  • Successfully matches and retrieves canonical LEIs from the synthetic GLEIF API data. Merges vessel manifest...
  • Dashboard displaying reconciled vessel manifests, LEI matches, and alerts for orphaned maritime entities.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 2 configured tools

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Mastra AI · Optional
Orchestration
  • Mastra AI · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Vessel-Owner Legal Entity Evidence Briefing Tool