0
%
Overall efficiency improvement versus the manual approach
16
hr
Manual hours reduced through AI-assisted classification
Identifying and Classifying Clean Tech and Climate Tech Fund Investors for a Global Investment Bank
About the Client
A global investment bank sharpening its clean tech deal pitching strategy
engaged TresVista to enhance its deal pitching strategy by building a curated, validated list of dedicated clean tech and climate tech investors, drawn from recent deal activity and filtered to exclude irrelevant investor and deal types..
Business Challenge
Four barriers to accurate, efficient fund classification
• Manual deal analysis at scale
Analysing clean tech and climate tech deals to identify fund investors was manual and time-intensive, requiring detailed review across the target deal universe
• Subjective categorization risk
Categorizing investment vehicles while excluding individual investors, operating companies, and non-equity funding required subjective judgment, creating inconsistency risk across the dataset
• Complex deal-type filtering
Filtering relevant deals to equity transactions only, while excluding project financing and debt, added a layer of complexity to an already detailed research process.
• Consistency under time pressure
Manual classification and deal-type screening created time pressure, increasing the risk of inconsistent categorization across the investor universe
Strategic Approach
TresVista's Solution: AI-assisted classification powered by predefined exclusion logic and human validation
TresVista designed and implemented an AI-assisted investor identification and fund classification framework, combining predefined exclusion logic with structured outputs and human-led validation to deliver an accurate, analysis-ready investor list.
01
AI-Assisted Investor Identification
TresVista used ModelML to identify fund investors from the target deal universe, automating the initial identification phase across clean tech and climate tech deal activity.
02
Automated Exclusion Logic
Predefined exclusion logic was applied to filter out non-equity deals, project financing, and debt-related transactions, along with individual investors and operating companies, prior to classification.
03
Structured Fund Classification
TresVista classified remaining investors into predefined buckets, organizing the dataset into structured, analysis-ready output tables aligned with the client's expected format.
04
Human Review and Validation
TresVista analysts reviewed outputs to validate classification accuracy, resolve edge cases, and confirm final quality before the investor list was delivered.
TresVista Impact
A curated investor universe. Reusable exclusion logic. 18% less time.
Before
16
hours
After
13
hours
18%
less time
~ 3 hrs Saved per engagement
Reinvested in refining pitch targeting and outreach.
Outcome
- Reduced execution time by approximately 18%, cutting the engagement from 16 hours to around 13 hours
- Generated a comprehensive investor list with reduced manual effort, covering the full clean tech and climate tech deal universe
- Accelerated deal filtering and structured fund classification, improving consistency across investor categorization
- Delivered analysis-ready tables with reusable logic, enabling faster re-runs and scope expansion for future deal cycles
Tools & Technologies
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