Automating End-to-End VC Deal Screening
Less manual processing
The Problem
A UK-based enterprise venture capital firm receives deal flow from multiple sources: PitchBook exports, inbound applications, referrals from portfolio companies, and legacy databases accumulated over years of operation. The raw data was a mess. Over 30,000 rows of company records needed cleaning, deduplication, and classification before the investment team could even begin evaluating opportunities.
The manual processing was unsustainable. Analysts spent weeks each quarter reconciling data from PitchBook with internal records, identifying and merging duplicate entries, classifying companies by sector and stage, and flagging which ones matched the firm's investment thesis. By the time the data was clean enough to work with, some of the most promising opportunities had already been funded by faster-moving competitors.
The firm needed a system that could take raw, messy deal flow data from any source and automatically produce a clean, deduplicated, classified, and prioritised list of companies worth evaluating. The investment team wanted to spend their time making investment decisions, not wrangling spreadsheets.
The Solution
BetterBrain built an automated deal screening pipeline that consolidates data from PitchBook exports and the firm's legacy sources into a single, clean dataset. The system applies custom deduplication logic that goes beyond simple name matching, using company descriptions, founder names, locations, and funding history to identify duplicates with high confidence.
Once deduplicated, the system enriches each company record with data from LinkedIn and other external sources, filling in gaps in the original data. It then applies the firm's investment thesis as a filter, automatically classifying companies by category and generating priority scores based on how well each company matches the firm's criteria. The scoring model was built in close collaboration with the investment team to capture the nuanced judgment that experienced investors apply.
The output is a ranked list of companies ready for human evaluation, complete with enriched profiles and clear explanations of why each company scored the way it did. The investment team reviews the top of the list, confident that they are not missing promising opportunities buried in messy data.
The Number
- Over 90% reduction in manual data processing
- 30,000+ company records cleaned, deduplicated, and classified automatically
- Data enrichment from LinkedIn and external sources at scale
- Investment team focused on decision-making instead of data wrangling
- Faster response to promising deal flow before competitors