Over the past few decades, public equity investment has become more and more about building a data and operations factory, allowing firms to take in data from hundreds of data sources to conduct discovery, research and portfolio management at scale. However, that transition has been harder to come by in private equity investing given the lack of clean and complete data on private companies.
Until now...
AI is rapidly closing this operational gap. The power of inference in AI is enabling operations focused PE firms to create scalable investment and outreach processes. AI tools can create better and more flexible data acquisition pipelines and tools for the business development and investment research processes.
SOVA specializes in delivering AI systems that generate high-quality leads, helping PE firms improve efficiency and research coverage. Our solutions enable more effective, natural language search through your internal private company and people data using AI assisted search techniques. No matter where you are on your AI journey, we’ve got you covered.
SOVA drives growth for PE firms and their portfolio companies by enhancing scale and precision of research and investment processes. We cater to the needs and objectives of various stakeholders, from CEOs seeking competitive edge to CMOs aiming for differentiation and revenue growth, CIOs prioritizing safe and secure tech integration, CISOs ensuring system security, and COOs enhancing team efficiency.
Our solutions include Company Prospector Chatbots, AI Assistants or Modules, and customized tools for ranking and prioritizing prospect companies with minimal or messy data. With SOVA, PE firms can research companies deeply and broadly simultaneously, gaining a strategic advantage in the market.
Quite often, the data sources from varied systems comprise both structured and unstructured data, and generating recommended actions requires a balance of weighted inputs to derive optimal remediation outcomes.
At this Strategy Private Equity (PE) firm, we implemented a Generative AI solution architecture that could source and generate recommendations after synthesizing both types of data, originally sourced from multiple systems. The impact was strong, improving prospect rate on sales outreach from 1% to 5%.
