Privatization M&A Analysis Workflow with Mastra AI & Factory AI
Analyzing US-led investment consortiums prequalifying for Saudi sports privatisation requires strict financial valuation and legal rule validation. Build a TypeScript agent workflow using Mastra AI integrated with Factory AI autonomous codegen tools to automatically extract asset metrics, run cash flow models, and output investment risk scores. Ensure valuation precision within a 2% error margin.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an automated M&A valuation agentic workflow for Saudi sports privatisation using Mastra AI and Factory AI tools.
How work is evaluated
Evaluates accuracy of financial cash flow modeling and regulatory compliance scoring generated by Mastra AI agent.
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.
Ownership Cap Verification
Flags compliance false if foreign equity exceeds 49% limit under Ministry regulations.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Valuation Precision
Measures variance between calculated DCF and ground truth DCF benchmark. • target: 0.98 • 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
Construct Mastra AI step-based workflows for financial analysis
Integrate Factory AI code generation primitives to execute dynamic DCF math
Validate Saudi ministry regulatory compliance rules programmatically
Output structured privatization bid evaluation reports
Reference links and supporting material
Financial reports and regulatory ownership rules for 10 Saudi sports clubs undergoing privatization.
Challenge setup
Evaluates accuracy of financial cash flow modeling and regulatory compliance scoring generated by Mastra AI agent.
Challenge input
JSON object with revenue_history, discount_rate, ownership_structure_pct
Mastra AI
TypeScript framework requirement.
Factory AI
Used for dynamic financial modeling code execution.
Evaluated output
JSON object with dcf_valuation_usd, foreign_ownership_compliant, risk_score
- Flags compliance false if foreign equity exceeds 49% limit under Ministry regulations.
- Measures variance between calculated DCF and ground truth DCF benchmark. • target: 0.98 • range: 0-1
- Benchmark: FinAgent-Bench
- Valuation Precision target: 0.98
- 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
- Factory AI · Optional
- Sacred · Optional
- Mastra AI · Required
Evaluation contract
- Ownership Cap Verification · Weight 1
- Valuation Precision · Weight 1
Metrics
- Valuation Precision · Target 0.98
Benchmarks
- FinAgent-Bench
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with revenue_history, discount_rate, ownership_structure_pct
Output: JSON object with dcf_valuation_usd, foreign_ownership_compliant, risk_score
Execution harness: JavaScript
Evaluation tools
- Mastra AI · Agent Orchestration Engine
- Factory AI · Autonomous Code Generator
Recipe freeze marker
No recipe freeze time is recorded.