0
%
Reduction in execution time across the screening exercise
0
+
GCC/MENA listed companies scored and classified
$
0
b+
AUM of the global PE firm served
Screening 72+ Companies with a Standardized Infrastructure Scorecard Framework
About the Client
A Leading Global PE Firm building a structured view of GCC/MENA infrastructure
with $750B+ in assets under management engaged TresVista to support its infrastructure team in assessing listed GCC/MENA companies against core infrastructure characteristics, using a standardized framework to enable consistent, scalable evaluation across a large universe.
Business Challenge
Four barriers to consistent infrastructure screening
• Large and fragmented universe
A wide universe of listed infrastructure companies across GCC/MENA required systematic review, with no pre-existing framework to guide consistent assessment
• Dispersed disclosure sources
Relevant data was spread across filings, annual reports, investor presentations, and online sources, requiring significant effort to locate and consolidate for each company
• Time-intensive manual review
Without a structured extraction process, manually assessing each company against infrastructure criteria was resource-heavy and difficult to sustain at scale
• Cross-sector scoring consistency
Applying infrastructure characteristics consistently across diverse sectors and geographies required a disciplined scoring methodology to avoid subjective variation
Strategic Approach
TresVista's Solution: A five-parameter scoring engine, powered by AI-assisted extraction
TresVista designed and implemented a structured infrastructure scorecard framework, combining a five-parameter assessment methodology with AI-assisted disclosure extraction to deliver consistent, analyst-validated scorecards at scale.
01
Infrastructure Scoring Methodology
TresVista developed a standardized scoring framework built around five parameters that define infrastructure companies, ensuring a consistent and replicable basis for assessment across the full universe
02
AI-Assisted Disclosure Extraction
ChatGPT was used to accelerate extraction of relevant data points from company filings, annual reports, investor presentations, and public sources, reducing the manual effort of sourcing evidence for each parameter
03
Standardized Scorecard Production
TresVista produced structured scorecards for each company in the universe, documenting parameter-level scores and supporting rationale in a format designed for direct use by the client's investment team
04
Analyst-Led Review and Alignment
TresVista analysts reviewed and validated all scorecards against source material, applying investment judgment to ensure classification accuracy and alignment with the client's infrastructure lens before final delivery
TresVista Impact
72 scorecards. A reusable framework. 60% less time.
Before
70-90+
hours
After
30-40
hours
60%
less time
~70 hrs Saved per engagement
Reinvested in deeper analytical work.
Outcome
- Standardized infrastructure scorecards delivered for 72+ companies, classifying each into Core Infrastructure, Infrastructure-Like, or Non-Core / Adjacent tiers with documented parameter-level rationale
- Approximately 60% reduction in execution time, cutting manual effort from 70–90+ hours to 30–40 hours while maintaining rigour and consistency across the full universe
- Faster analyst review and alignment, with a reusable scoring framework that can be extended to new companies as the client's GCC/MENA infrastructure pipeline grows
Tools & Technologies
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