[Test] How modern data engines are changing the rules of private equity

Private equity and corporate finance have spent the last decade layering on CRMs, datasets and dashboards, yet many teams still struggle to see a single, joined-up view of their market. In our recent data engines webinar, VP of Growth Alex Bajdechi noted that firms are now juggling “Expert network data, company data, fundraising data, proprietary data sitting across Excel data rooms” without a coherent way to connect it all. 

The opportunity now is to turn this sprawl into a true data engine that supports how dealmakers actually work. To explore what that shift looks like, Filament Syfter CEO Phil Westcott moderated a discussion with Bajdechi who was joined by John Farrugia, CEO of Cavendish Group and Pete Morrison, Head of Technology at Vitruvian. 

As Phil put it, “Today, we're looking at what leading firms are building to give their dealmakers an edge.” Three themes stood out:

1. It’s been a long journey from point solutions to connected ecosystems

Private equity and M&A firms have embraced specialist tools, but many are now feeling the drawbacks. John captured the mood bluntly: “I've had enough of point solutions. There's too many point solutions out there.” Disconnected systems create duplicated effort, inconsistent lists and partial views of each opportunity.

Alex described how this happened: new feeds and providers were added over time — 

Expert network data, company data, fundraising data, proprietary data sitting across Excel data rooms

— but rarely designed into a single architecture. The path forward is to consolidate these sources and make them AI-ready, so firms can operate from a unified data layer. A data engine like Filament Syfter sits across CRM, internal knowledge and third-party sources, turning scattered information into a live, firm-wide market view.

2. Data engines have the power to reshape how deal teams work

A data engine is not only a technology choice; it changes how deal teams spend time. John described how an agentic AI stack rebuilt a complex buyer list in hours rather than weeks, with more comprehensive coverage than a traditional manual process could deliver. On the private equity side, Pete’s journey started with a deliberate investment in capability:

“Yeah, so I guess our journey started probably 5 years ago, when we hired our first couple of data scientists.” His team went on to experiment with “different use cases that we could apply machine learning and AI to,” allowing them to support both deal origination and execution with the same underlying data engine. As these capabilities mature, culture shifts. Pete’s mantra is to “drive your use cases from the business,” letting the engine and agents assemble the right information so dealmakers can focus on judgment and relationships.

3. Getting AI-ready means getting your house in order

Many firms want to jump straight to generative AI, but the panel stressed that foundations matter. Alex cautioned that while firms “naturally want to get AI in the door,” to do that “you kind of have to have your house in order” — starting with data quality, permissioning, security and clear flows between systems.

On the talent side, Pete argued that “from my point of view, you need a really strong data scientist.” Whether in-house or via a specialist partner, that capability needs to sit close to the business, not off to one side. John’s experience echoed that; choosing the right implementation partner can accelerate the journey dramatically: “It might cost you a bit more money, we found that, but you get what you want far, far more quickly.”

For mid-market firms still in the “exploring” or “piloting” stage, a pragmatic starting point is a single, high-value commercial use case and the data sources that support it.

So what’s a firm to do?

Across the conversation, one message was consistent: in an increasingly crowded market, advantage will belong to firms that can unify their internal knowledge with external data and make it usable through AI. Tools on their own will not win mandates; a connected, AI-ready data engine will.

Filament Syfter was built for exactly that purpose: an AI-enabled deal engine for private equity and corporate finance that turns fragmented data into a live, actionable market view. To dive deeper into these themes or see how a data engine could work inside your own firm, explore the on-demand webinar or contact the Filament Syfter team to arrange a tailored walkthrough.

If you’d like to see how a modern data engine can unify your internal knowledge and external market data, book a demo with the Filament Syfter team to explore how leading firms are building a live, AI-ready market view.