Industry Trends:

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. 

Enabled by AI and unstructured data processing, the future of Private equity looks a lot more like public equity counterparts do today. 

• AI data collection is enabling operations focused PE firms to gather strategy focused data on even the smallest companies.
• AI enabled data enrichment allows for deeper data collection for companies in evaluation or early discovery pipeline.
• A more robust data foundation enables more robust qualification and outreach, investment analysis and portfolio reporting. 

New technologies often create an economic feasibility shift. We see this today in how more companies in private equity are able to take control of a larger part of their data management.

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Why SOVA:

• Custom Data Collection: Better document pre-processing for better answers.  OOTB providers are focused on a broad set of customers while SOVA can give your contract, policy and legal data the specific attention it needs to optimize performance of AI systems.
• Unlimited Workflow Integration: Data is only useful when integrated into your workflows. As a system agnostic partner, we can leverage the best of what you have today, as well as recommend and implement new systems to improve your efficiency.
• Bespoke Applications: Our tech-enabled services team can create custom AI applications for your unique workflow requirements and other challenges.
• AI Co-Worker Agents: Our advanced, engineered technology allows you to build AI agents for endless applications including deal sourcing evaluation, back-office applications, regulatory and compliance, and contracts.

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Select Case Study:

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Client

Private Equity Firm

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Problem

An internal database of companies and people  was large and expanding in data sources. There was critical data in-house for business development and investment research teams but it was difficult to leverage given data structure and need for complex SQL queries. Because of the many sources of data, key information can be captured and structured in a number of ways. The lack of data accessibility was restricting the ability to use data assets to improve operations.

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Plan

Leverage AI based search and data cleansing in order to create a chat based application that can help users query internal data stores. Improve both the accessibility of data and the quality of search results through novel search methods.

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Results

The SOVA developed AI application is being used as a critical part of the business development process resulting in a 5x improvement in lead quality. High lead quality is expected to enable an overall increase in total acquisitions per year, enabling expansion by removing a critical bottleneck.

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