Cross-Border Maritime Trade Discrepancy Resolver with CrewAI and Groq Cloud
Build an automated cross-border trade document review crew using CrewAI and Groq Cloud high-speed inference. Reconcile bills of lading, customs declarations, and port clearance records to accelerate vessel turnaround at Singapore ports.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate multi-role CrewAI agents running on Groq Cloud ultra-fast inference to detect discrepancies across maritime trade documents.
How work is evaluated
Evaluates identification of weight mismatch, HS code discrepancy, and cargo clearance recommendations.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
discrepancy_detected
Ensures weight mismatch triggers clearance hold
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
execution_speed_ms
Time taken to complete full crew evaluation cycle • target: 1200 • range: 0-3000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design collaborate multi-agent workflows using CrewAI Agent, Task, and Crew primitives
Configure Groq Cloud API connections for sub-second inference across document comparison tasks
Implement trade document validation logic matching HS Codes, weights, and consignee data
Generate automated clearance hold or permit approval notices for Singapore customs
Reference links and supporting material
Synthetic dataset of 30 paired shipping manifests and Singapore customs entry records.
How this agent runs
Evaluates identification of weight mismatch, HS code discrepancy, and cargo clearance recommendations.
Challenge input
JSON containing bill_of_lading and customs_declaration objects
CrewAI
Orchestrates dedicated agent roles for manifest and customs auditing.
Groq Cloud
Delivers sub-second response times for complex trade validation.
Evaluated output
JSON containing clearance_status, discrepancy_found (boolean), and discrepancy_details
- Ensures weight mismatch triggers clearance hold
- Time taken to complete full crew evaluation cycle • target: 1200 • range: 0-3000
- Benchmark: Logistics Efficiency Benchmark
- Execution Speed Ms target: 1200
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
- Groq Cloud · Optional
Evaluation contract
- discrepancy_detected · Weight 1
- execution_speed_ms · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.