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Agentic Dealflow Scoring is one of the AI applications in VC with the fastest time to value. We see it being used across a broad base of customers at different stages and geographies, and in both equity and debt strategies. By systematically combining external data sources, proprietary insights, and self-reported startup information, it enables investors to identify and prioritize opportunities with greater consistency and speed.
1. Most Common Use Cases for Agentic Dealflow Scoring
Outbound Dealflow Prioritization (Prior to Outreach):
Agentic dealflow scoring helps VCs rank the most promising outbound leads before initiating contact by analyzing external signals such as traction, founder background, and market potential. This enables teams to focus their outreach efforts on the highest-probability opportunities.
Inbound Dealflow Analysis (Including Self-Reported Data from the Deck):
By applying agentic scoring to inbound deals, VCs can evaluate startups using both structured metrics and unstructured information extracted from pitch decks. This approach ensures consistent evaluation standards and accelerates the triage process.
Competitive Analysis (Ranking Based on Competitors Investments):
Agentic dealflow scoring allows firms to benchmark and assess opportunities that competitors have invested in using their own proprietary evaluation models. This helps identify blind spots, reveal market trends, and refine strategic positioning.
2. Most Common Data Sources used by the Agent
Self-reported data: Deck, Business Plan, Call Notes, Survey responses or application forms
Private Databases (behind a paid API): Funding data, corporate registry data, people data, web traffic and SEO data, hiring data and job analytics, technographic and product-usage data, patents and IP databases.
Publicly available information (direct or through AI engines): News articles and interviews, press releases and company announcements, market/industry reports, research papers or technical documentation, website content and metadata, public filings, public job postings.
3. Most Popular Categories for Dealflow Scoring using AI Agents
Geography: Geographical criteria are often a key part of a VC’s investment strategy. Applying geographical filters helps narrow the focus and cut through the noise.
Most common
- Country of Incorporation
- Where are the founders located
- Where are the employees located
Pro Tip: Companies are often distributed across several regions and may be incorporated in jurisdictions different from where the founders live. Focus on the place of residence of the founders and employees to identify the geographies where the company has meaningful presence and critical mass.
Sector: Sector or domain criteria form the foundation of a VC’s investment strategy. Most firms develop their own unique taxonomy, reflecting their focus and expertise. These definitions can be taught to the agent, enabling it to think and reason like an in-house analyst.
Most common
- Deep-Tech
- Artificial Intelligence
- Defense / Dual-use
- Climate tech
Pro Tip: Do not rely solely on standard taxonomies from data providers. These represent the industry’s common denominator and may not reflect your unique investment strategy. Instead, provide your own taxonomy with clear definitions and detailed descriptions of each sector, along with company examples.
Traction: Depending on whether the workflow is using self-reported data from the startup or just external signals or a combination of both, this belongs to some of the most common categories of criteria applied.
Most common
- Headcount growth (Last 6-month Growth)
- ARR growth (self-reported from the Deck)
- Web traffic growth (QoQ Growth)
Pro Tip: Be mindful of a new breed of companies that do not need nearly as many employees as before. Their FTE growth might be misleading.
Team and Management: By far the most common and broad category of criteria. Ranging from academic results, to prior founder experience and team completeness. The depth and breath of this category is almost endless and depends heavily on the sector focus.
Most common criteria
- Product-Team fit (Scale from 0-10)
- Repeat founder (True / False)
- Domain tenure (Scale from 0-10)
Pro Tip: Think carefully about your sector focus before deciding on the team criteria you’re looking for. The ideal founding team for a defense startup is very different from one for biotech or fintech.
Funding and Investors: Funding activity is used by investors as a signal of market validation and investor confidence. The quality and reputation of participating investors, along with the momentum and cadence of capital raises, can indicate both the likelihood of future financing opportunities.
Most common criteria
- Amount raised
- Time since last funding
- Number of funding rounds
- For Venture Debt: Strength of equity sponsorship (Scale 0-10)
Pro Tip: Excessive focus on adverse selection can obscure relevant indicators of quality. The participation of top investors generally signifies confidence and strength, rather than suggesting potential adverse selection.
4. Most popular Agentic Criteria by stage
Pre-Seed and Seed:
The agent’s primary focus is on the sector and the team, including their academic background, past experience, and tenure working together. At this stage, it is common for startups to experience some degree of team fluctuation as roles and alignment evolve.
Series A:
Agentic evaluation combines sector and team criteria with traction indicators derived from both external sources and self-reported data. Competitiveness and market dynamics may also be considered, though they tend to be less decisive at this stage.
Series B and Later:
The emphasis of the agentic work shifts toward traction, validated through external data and self-reported performance metrics. Market research, competitive landscape, and industry dynamics become increasingly important in assessing scalability and long-term sustainability.
Venture Lending:
For venture debt, the agent’s focus is primarily on the strength of equity sponsors and the company’s self-reported financial data. Team experience and credibility remain relevant, but the emphasis is less on innovation and more on tenure, stability, and operational maturity.
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