Client Overview

Our client is a European Private Equity (PE) firm who invest in fast-growing companies across all sectors. PE is an intensely competitive and research intensive sector. Firms need to cover target sectors and companies using qualitative judgment as well as complex quantitative models, which are defined by the firms’ investment criteria. Typically, one member of staff will cover 15-25 companies to really track that company closely. This makes it hard for mid market firms to cover their whole market, and as a result it is difficult to find every relevant opportunity and generate above market returns.

Challenge

Deal origination is a critical part of the investment lifecycle and finding great investment opportunities depends upon having the right data points, at the right time, and before anyone else. As such our client needed to optimise and expand their research to identify potential investment targets and continuously monitor the evolution of companies in their dealflow, without scaling the team.

PE firms need to constantly analyse a number of internal/external unstructured and structured data points for positive investment signals. This labourious and resource intensive process, can be enhanced with machine learning (ML).

The client required an intelligent, scalable solution that would detect investment signals and opportunities customised to their investment thesis, thus improving the efficiency of their deal origination process by surfacing companies which have the highest probability of becoming good investments.

Solution

The solution that Filament AI delivered to the PE firm aggregates and continuously monitors a variety of data sources in a single, easy to use platform; a platform which even integrated with their CRM system. Custom natural language processing (NLP) and ML models powered the recommendation engine, which presents investment opportunities, based on company features, metrics and signals, and continuously learn from user interaction. Filament AI’s dedicated framework for incorporating these models into a deal origination dashboard has transformed the research process from a pull based model to a push based solution by predicting and presenting the most relevant information in real-time.

Outcome

The platform is now live and used every day by half of the organisation, allowing them to cover more ground in deal origination research while analysing more data points in initial and follow-up investment decisions. Our client has bottled the IP of their investment thesis through the creation of custom ML models, integrating this with their CRM system provides insight on their portfolio and funnel in real-time.  Instead of tracking a handful of companies as done previously, the firm is now tracking all companies which meet their investment criteria across Europe without needing to grow the size of their team.

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