Build a Cross-Border Entity Resolution Workflow with Mastra AI and ZenML
Incomplete entity master data hampers cross-border payment processing and creates regulatory vulnerabilities. Build a Mastra AI agent workflow managed by ZenML pipelines to perform entity resolution, standardize LEI records, and cross-reference ultimate beneficial ownership trees. Deliver a automated entity reconciliation engine achieving greater than 95% precision on dirty enterprise records.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a TypeScript Mastra AI agent framework integrated into a ZenML MLOps pipeline for automated corporate entity data cleaning and disambiguation.
How work is evaluated
Evaluates the agent's ability to resolve ambiguous company names to exact canonical LEI codes.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
lei_format_check
Checks if output contains a valid 20-character alphanumeric LEI format
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
precision
Precision of resolved LEI matches against ground truth • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a TypeScript Mastra AI agent for structured entity resolution and deduplication
Orchestrate training and inference steps using ZenML workflow pipelines
Match noisy enterprise trade records against Global Legal Entity Identifier Foundation (GLEIF) databases
Construct knowledge graph links for Ultimate Beneficial Owner (UBO) discovery
Reference links and supporting material
Dataset of 400 noisy cross-border entity records paired with ground-truth GLEIF Legal Entity Identifier records.
Challenge setup
Evaluates the agent's ability to resolve ambiguous company names to exact canonical LEI codes.
Challenge input
JSON object with raw_name, address_snippet, and country
Mastra AI
Offers TypeScript native agent structure and flexible memory integration.
ZenML
Provides structured workflow pipelines for entity data resolution training and inference.
Evaluated output
JSON object with resolved_lei, canonical_name, confidence_score
- Checks if output contains a valid 20-character alphanumeric LEI format
- Precision of resolved LEI matches against ground truth • target: 0.95 • range: 0-1
- Benchmark: Entity Resolution Standard Benchmark
- Precision target: 0.95
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
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
- Mastra AI · Required
- ZenML · Optional
- Mastra AI · Required
Evaluation contract
- lei_format_check · Weight 1
- precision · Weight 1
Metrics
- precision · Target 0.95
Benchmarks
- Entity Resolution Standard Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with raw_name, address_snippet, and country
Output: JSON object with resolved_lei, canonical_name, confidence_score
Execution harness: JavaScript
Evaluation tools
- Mastra AI · Agent Framework
- ZenML · MLOps Pipeline Automation
Recipe freeze marker
No recipe freeze time is recorded.