Venture Capital
Improved early-stage investment sourcing with structured company signals.
Forestay Capital is an early-growth stage Enterprise AI and SaaS fund investing across Europe, Israel, and the East Coast of the United States. Forestay focuses on companies at inflection points, typically between Series A and C, where understanding a company's trajectory and timing engagement correctly are central to the investment process.
To support this, Forestay has built an internal data infrastructure that aggregates multiple sources and powers a recommendation engine used for sourcing and evaluation. PredictLeads data is integrated into this system to provide additional company-level signals.
At the stage where Forestay invests, companies often do not yet have extensive public visibility. Many relevant developments are not immediately reflected in traditional data sources such as funding announcements, press coverage, or financial disclosures.
As a result, investment teams need to rely on indirect indicators to understand whether a company is entering a growth phase, how it is evolving operationally, and whether it aligns with their investment thesis.
Forestay's approach is to combine multiple datasets into a unified view of each company. Within this setup, the role of PredictLeads is to contribute structured signals that reflect ongoing company activity.
PredictLeads data is integrated into Forestay's central data layer via API. The integration is part of a broader system that includes sources such as PitchBook, Harmonic, and internally developed models.
Once ingested, the data is processed and made available in two primary ways:
This setup allows signals from PredictLeads to be analyzed alongside other data points rather than in isolation.
Forestay uses PredictLeads data to enrich company profiles with information that reflects current activity. This includes signals derived from job openings, news events, and website changes.
Among these, job opening data has proven particularly useful. Hiring patterns provide insight into how a company is allocating resources and where it is focusing its efforts. For example, an increase in hiring in sales or go-to-market roles may indicate preparation for commercial expansion, while growth in engineering or data teams may reflect product development priorities.
By observing these patterns over time, the team can form a more detailed view of a company's trajectory.
PredictLeads data is used as an additional signal layer within Forestay's sourcing process. It contributes to identifying companies that may not yet be widely visible but show indications of growth or strategic activity.
In practice, this means that companies flagged by the internal recommendation engine can be further evaluated using PredictLeads signals. Hiring activity, in particular, has helped highlight companies that are scaling operationally before this is reflected in more widely distributed information.
This provides earlier visibility into companies that may become relevant investment opportunities.
One of the practical outcomes of using PredictLeads data is improved timing in engaging with companies.
Because signals such as hiring activity appear earlier than formal announcements, Forestay can identify changes in company behavior before they become broadly visible. This allows the team to initiate conversations with founders at an earlier stage, often before structured fundraising processes begin.
Earlier engagement can be particularly important in competitive markets, where access and timing influence the ability to participate in investment rounds.
From an implementation perspective, Forestay noted that PredictLeads data is straightforward to integrate. The APIs are clearly structured and well documented, which simplifies ingestion into existing systems.
The fact that the data is already categorized and linked to company domains allows it to be used directly within internal models and dashboards without requiring extensive preprocessing.
Forestay identified a few areas where additional data could further support their use case. These include signals related to company participation in events or conferences, as well as more detailed information about founders, such as professional background data.
These additions would complement the existing dataset by providing further context for evaluating companies and their leadership.
Forestay uses PredictLeads as part of a broader data infrastructure designed to support sourcing and evaluation. The dataset contributes structured, real-time signals that complement traditional sources and help provide a more complete view of company activity.
In particular, hiring data offers a forward-looking perspective on how companies are evolving, which supports earlier identification of potential investment opportunities and more informed timing of engagement.
PredictLeads provides structured company data derived from sources such as job openings, news events, and website changes. The data is linked to company domains and designed for integration into internal systems, enabling teams to monitor company activity and incorporate real-time signals into their workflows.
Learn more: PredictLeads API documentation