Signal-Based Sourcing Explained: The Future of VC Deal Flow

The venture capital industry has a sourcing problem that nobody talks about honestly. The vast majority of deals still come through warm introductions, existing relationships, and conference hallway conversations. This model worked when there were 500 active VC firms. It does not work when there are 5,000.

What Is Signal-Based Sourcing?

Signal-based sourcing is the practice of identifying investment opportunities by systematically monitoring data signals that predict company growth, rather than relying on relationship-driven inbound deal flow.

Instead of waiting for a warm intro, you track signals: a company that just tripled its engineering headcount, saw a 400% increase in web traffic, got featured in three industry publications, and posted six new enterprise sales roles. Those signals, taken together, paint a picture of a company that is accelerating — and one that might be raising capital in the next 3-6 months.

Why Signals Beat Intros

Coverage. Your network, no matter how strong, covers a fraction of the market. Signals cover everything. Every company that posts a job, ships a product, or gets written about generates signals that can be tracked.

Speed. By the time a warm intro reaches your inbox, the company has usually talked to 3-5 other funds already. Signal-based sourcing identifies companies before they start their fundraise, giving you the first-mover advantage.

Objectivity. Warm intros are filtered through the biases of the introducer. Signals are neutral. A company with strong hiring velocity and accelerating traction scores well regardless of who the founders know.

Consistency. Relationships ebb and flow. Some months your network is active, some months it is quiet. Signals are always on. Every day, new data points emerge across thousands of companies.

The Signal Stack

The most effective signal-based sourcing systems track multiple signal categories simultaneously:

• Hiring velocity: The single strongest leading indicator of company growth. Companies hire in advance of revenue, not after it.
• Funding activity: New rounds, bridge financings, and investor syndicate changes.
• Traction indicators: Web traffic, app store rankings, customer growth, revenue proxies.
• Press and mentions: Media coverage, awards, conference appearances.
• Product launches: New features, API releases, platform expansions.
• Market timing: Sector momentum, regulatory changes, competitive dynamics.

No single signal is definitive. The power is in the combination. A company with strong hiring AND accelerating traction AND recent press is a fundamentally different signal profile than a company with just one of those.

Who Is Doing This Well?

The best funds in the world have been doing versions of this for years, but with armies of analysts and expensive data tools. a16z has a dedicated data team. Tiger Global built internal tools to track company metrics at scale. These funds understood that data-driven sourcing creates structural advantages.

The difference now is that AI agents can deliver similar capabilities to funds of any size. You do not need a 10-person data team. You need a system that monitors signals and scores them against your thesis. That is what Ted does.

Getting Started

The first step is defining what signals matter most for your investment thesis. If you invest in B2B SaaS at Series A, hiring velocity and enterprise sales hiring might be your top signals. If you invest in consumer at seed, app store rankings and social media growth might matter more.

Signal-based sourcing is not a replacement for relationship building. It is a complement. The best VCs combine systematic signal monitoring with deep founder relationships. They use signals to find companies early, then use relationships to win the deal.