Automated Target Screening for a top-tier VC
- Benedikt Anselment
- Feb 10
- 4 min read
An early-stage venture capital firm focused on investing in technology-driven startups across Europe operates in a highly dynamic environment shaped by a constant flow of new company formations. Identifying promising investment opportunities at an early stage requires continuous market monitoring, structured evaluation, and informed decision-making across a broad and diverse startup landscape.
As deal flow increased over time, the firm reviewed how its target screening and early evaluation processes were organized and how technology could better support the investment team. To maintain high decision quality despite a rapidly growing deal flow, the company set out to structure and automate its target screening process, reduce manual effort, and focus time on the most relevant investment opportunities.
Challenge
The target screening process faced structural challenges that consumed significant time and made early-stage evaluation inefficient.
First, deal sourcing was highly manual and time-intensive. The team scraped and reviewed thousands of newly founded startups every month, requiring substantial manual effort to collect, structure, and assess basic information. This made the process slow and repetitive, with a high risk of information being missed or lost along the way.
Second, it was often unclear whether the time invested paid off. Many startups turned out to be irrelevant only after extensive manual screening, leading to inefficient use of resources and limited transparency on which targets truly matched the investment thesis. As deal volume increased, prioritization became harder and the overall process difficult to scale.
Solution
To address the challenges above, the company partnered with NOA to automate the target screening process using Low‑Code‑Tools and leveraging their Google Suite:
MAKE for automation orchestration
Make.com acted as the central automation layer orchestrating the entire target screening workflow. Instead of manually collecting, reviewing, and transferring data between tools, Make connected all relevant systems and ensured that each step followed a clear, automated logic.
Whenever new startups were identified, Make triggered the enrichment process automatically: scraping tasks were initiated, data was processed, and results were prepared for evaluation. This removed manual handovers, reduced repetitive work, and allowed the team to screen thousands of early-stage companies each month without increasing operational effort. As a result, target screening became faster, more reliable, and scalable by design.
Scalable data enrichment
To enrich investment targets with relevant insights, the company leveraged a combination of ScrapeNinja and Exa.ai. Starting from a company’s website URL, key information such as business model, market focus, technology relevance, and founder details was automatically extracted from websites, databases, and public sources.
This enrichment layer enabled automated pre-filtering at scale. Companies that clearly did not match the investment thesis were filtered out early, while promising startups were flagged for manual review. By combining structured scraping with AI-powered search and extraction, the company gained a consistent, data-driven foundation for evaluating a high volume of very early-stage companies.
Central target management
All enriched data was written directly into Affinity, the CRM system used to manage investment targets and related information. This ensured that investment targets, founder profiles, and evaluation signals were stored centrally and remained accessible to the entire team.
By automatically syncing enriched startup data into Affinity, manual data entry was eliminated and transparency improved significantly. The investment team could focus on analysis and decision-making rather than maintaining databases, while all screening results were fully traceable and consistently structured. This turned Affinity into a single source of truth for deal flow, seamlessly connected to an automated target screening engine.
The new process in detail:
The target screening process starts with a company’s website URL as the primary input. This URL serves as the starting point for collecting all relevant information.
With this input, Make automatically triggers multiple scraping and enrichment tools (combination of regular web scraping tools and AI search engines) to ensure broad coverage and data completeness.
Company and founder data is extracted from websites, databases, and public sources, resulting in a structured dataset tailored specifically for very early-stage startups. While this was primarily done for the startups, the same process works to look into their limited partners.
Based on predefined criteria, the enriched data is automatically analyzed to assess each target’s relevance to the company´s investment thesis.
All data points are written directly into the CRM system (Affinity). Promising startups are automatically flagged for manual review, while clearly irrelevant companies are filtered out according to the previous analysis.
The result is a scalable, partially automated target screening process that allows the company to evaluate large volumes of early-stage startups efficiently while keeping final investment decisions firmly in human hands.
Business Results
The newly created workflow by NOA delivered substantial improvements to the target screening process:
Faster Target Identification
By automating the initial screening and data enrichment steps, the time required to identify relevant investment targets was significantly reduced. Instead of manually reviewing thousands of new startups, the team can now focus on pre-qualified companies that match the investment thesis. This accelerated early-stage evaluation and improved overall decision velocity.
Improved Data Quality
Automated scraping and AI-based enrichment ensured that each startup was evaluated based on a broad and consistent set of data points. Company, market, and founder information is now collected in a standardized way, reducing information gaps and improving comparability across targets. This created a more reliable foundation for early investment decisions.
Scalable Screening Process
The new process allows the company to screen large volumes of early-stage startups in a short period of time without increasing manual workload or headcount. Automation absorbs fluctuations in deal flow, making the screening process highly scalable while keeping operational effort predictable and efficient.
BOTTOM LINE
By partnering with NOA, the VC transformed a manual, time-intensive target screening process into a scalable, automated system that surfaces the most relevant startups faster and with higher data quality. This enables the investment team to focus on high-conviction opportunities while efficiently managing a rapidly growing deal flow without added operational overhead.
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