Why PE Firms Partner to Build AI-Enabled Data Engines

Private equity is entering the next phase of its AI journey, one defined by execution, not experimentation. According to the AI Pathfinder Private Equity Benchmark Survey, nearly half of firms are still only piloting AI use cases, and fewer than 1 in 10 have scaled them across the investment lifecycle.

That gap between ambition and adoption is exactly what Filament Syfter’s new guide Why every private equity firm needs a data engine (and how to build it) was built to address. It explores how leading firms are connecting their data ecosystems, operationalizing AI, and building sustainable competitive advantage through a new class of infrastructure: the data engine.

This article takes a closer look at one of the guide’s key themes; why more private equity firms are choosing to build with a technology partner instead of building entirely in-house.

What “build with” really means

“Building with” is a co-development model: your firm owns the strategy, taxonomy, and IP that differentiate you, while your partner supplies the data plumbing, matching logic, technology infrastructure/security and AI scaffolding that would otherwise take years to assemble and harden. You get a tailored engine faster - without accepting vendor lock-in or shouldering the full engineering burden.

Leverage PE-specific data engineering

Filament Syfter has already mapped and operationalized the messy and crowded reality of third-party market data. That means ready-made pipelines to ingest, normalize, enrich, and deduplicate data, plus battle-tested patterns for handling edge cases (ambiguous tickers, M&A history, entity rebrands, sparse financials). Instead of burning cycles on building “table stakes” capabilities, your team focuses on the proprietary signals that activate deals.

Gain best-in-class data matching and a true “source of truth”

Conflicting records derail confidence and adoption. With firmwide survivorship rules, hierarchies, and confidence scoring, Filament Syfter codifies how your organization decides which field wins, when, and why. Importantly, then the technology enforces those rules consistently across teams and tools. The result is clarity, not debate; auditability, not opinion.

Introduce (and train) your firm’s Virtual Analyst

Once data flows through your firm’s engine, your Virtual Analyst goes to work. It continuously consumes new signals, learning from user feedback and activity, and surfacing companies and themes aligned to your thesis. It triages noise and flags changes that matter (leadership moves, financings, product launches, etc.) so origination and IC prep start from a stronger baseline.

Configure custom scoring for your fund and bolt-on strategies

Translate your playbook into the engine: weightings for market structure, ICP fit, capital efficiency, geography, tech stack, channel leverage, you name it. Whatever drives conviction for your team is the fuel for the engine. The engine applies your firm’s rubric automatically, standardizing early diligence and letting associates spend more time on “why” and less on spreadsheet gymnastics.

Tune the taxonomy to match how your firm actually talks

Stop forcing your teams to think in a data vendor’s language. With a firm-native taxonomy, synonyms, and tags, searches and alerts reflect your vocabulary, including sector nuances, sub-verticals, and code words that appear in partner meetings and Monday standups. Adoption jumps when the system “speaks” like your deal teams.

Start connected: 30 market data sources and 8 internal systems out of the box

Speed matters. Building with Filament Syfter gives you native integrations to 30 commonly used market data providers and 8 internal fund systems (think CRM, portfolio monitoring, data lake/warehouse, ERP, email/calendar, and more). The library reflects what private equity teams actually use and trust, eliminating months of internal resource allocation and data wrangling.

Conclusion

For private equity firms, the strategic advantage isn’t in rebuilding ETL, matching, and AI scaffolding, it’s in codifying your unique investment judgment and scaling it across the firm. Building with Filament Syfter lets you own the differentiating layer while standing on proven engineering. That’s how you minimize risk, accelerate value, and ensure your AI-enabled data engine keeps compounding advantage quarter after quarter.

Interested in what “build with” would look like for your thesis and tech stack? Let’s map a 90-day path from raw data to a working Virtual Analyst and scoring rubric, tailored to your firm.