The case against pointing an LLM at a data lake for private equity

Across private equity, there’s a growing temptation to believe that modern AI can be reduced to a simple formula: load documents into a data lake like Snowflake, connect an LLM, and let the magic happen. But while that may be an appealing idea, it breaks down almost immediately in real-world PE environments - where data is complex, confidential, distributed, and deeply interwoven with firm-specific workflows.

That distinction - between storage and intelligence - is exactly where most DIY AI efforts falter.

Unstructured documents don’t become useful just because you store them

The first challenge is ingestion. Moving CIMs, PDFs, contracts, emails, and presentations into a data lake isn’t a drag-and-drop activity. It requires OCR, chunking, embeddings, re-embeddings, metadata design, and version control. Even more complicated: enforcing permissions that match every VDR, SharePoint site, email folder, and network drive.

For many firms, it’s actually far safer and faster to integrate with systems that already handle vector search and permissions - rather than attempting to recreate them.

Structured and unstructured data must be modeled, not just stored

Private equity analysis depends on synthesizing financials, CRM entries, market signals, diligence materials, and sector taxonomies. In a data lake, these assets sit side-by-side but not side-by-side in any meaningful sense.

Without entity resolution, semantic modeling, and consistent metadata, an LLM cannot reason across them reliably. The result is often inconsistent or misleading output - the opposite of “AI you can take into an IC meeting.”

A data lake is not a platform

Even once the data is inside the lake, firms still need pipelines, governance, lineage tracking, business logic, guardrails, workflows, and domain-specific taxonomy layers. Snowflake gives you storage and compute. It does not give you:

• Retrieval pipelines

• Investment logic

• Guardrails or validation

• Scoring frameworks

• Sector and thesis models

• User workflows

Those are the pieces that actually create differentiated intelligence.

Why hosting your own ML/LLM engine isn’t realistic for private equity firms

Some firms consider going further - hosting and maintaining their own ML environment. But this requires a true platform engineering function: ML engineers, DevOps, security engineering, monitoring, model evaluators, and product owners. It’s a 24/7 operational burden with compliance, reliability, and governance requirements far beyond typical PE IT capabilities.

Model innovation cycles also move too fast. New architectures ship every few months, which means constant upgrades, compatibility checks, and re-tuning. Most firms fall behind within a year.

The total cost of ownership ends up dramatically higher than using a production-grade intelligence platform.

A new path forward

For firms serious about long-term competitive advantage in sourcing, underwriting, and portfolio value creation, the answer isn’t “more storage” or “a bigger LLM.” It’s a fully engineered intelligence layer - complete with permissions, pipelines, domain models, workflows, and AI that is shaped to how private equity actually operates.

That’s the foundation Filament Syfter has built. And it’s why the firms that win with AI in the next decade won’t be the ones with the biggest data lake - they’ll be the ones with the strongest data engine.

The simplest way to understand the difference is to see in action, a short demo will show you why.