The Data-First Advantage: Why Sidekick Is Built on Structured Intelligence

Allocators and managers are under growing pressure; Due diligence questionnaires are getting longer, investor requests more detailed, and monitoring expectations increasingly complex. Many firms are turning to AI for relief, but not all AI delivers lasting value.

At Dasseti, we’ve seen the risks of taking an “AI-first” approach. Chat interfaces and black-box outputs may provide quick answers, but they don’t create the structured intelligence needed for long-term use. As Liron Mandelbaum, our COO, puts it:
“Data first eats AI first for breakfast.”

Here’s why Sidekick was built on a data-first philosophy, and why that matters for every allocator, consultant, and GP.

The Problem with AI-First Approaches

Most generic AI tools focus on generating answers quickly. That sounds helpful, until you try to compare responses year over year or reuse insights across different processes. Without structure, outputs become siloed, and firms are left starting from scratch each time.

Consider the common scenario where an LLM is asked to complete 30 due diligence questions at once. Each answer is influenced by the others, creating inconsistency and bias. The result: verbose, varied responses that can’t be trusted or reused.

For investment professionals who rely on auditability, comparability, and precision, that approach is risky, and, in a market where over 60% of firms now leverage machine learning for investment decision-making, failing to structure your data means missing out on industry best practices.

What Data-First Means in Practice

A data-first model ensures that every AI interaction feeds into a structured foundation that compounds over time. With Sidekick, every extracted answer, every flagged inconsistency, every generated draft is stored as structured data within manager, fund, or company profiles in COLLECT and ENGAGE.

This creates what we call the flywheel effect:

• Each year of due diligence adds more data points for comparison.
• Each RFP or DDQ response enriches the central content library.
• Each review builds a stronger, auditable trail for regulators, clients, and internal teams.

Instead of chat logs that disappear into the ether, firms build institutional knowledge they can draw on again and again.

Industry studies back this up: as data volumes soar - in financial services, global data creation is expected to hit 175 zettabytes by 2025 - the demand for scalable, structured approaches is more urgent than ever. Investment managers report that leveraging structured data improves auditability, accelerates compliance workflows, and enhances transparency for clients and regulators.

The Sidekick Difference

Sidekick is not another stand-alone AI tool. It’s:

• Embedded in workflows - integrated directly into due diligence, monitoring, and investor communication processes.
• Transparent and auditable - every output comes with source references, confidence scores, and a clear audit trail.
• Enterprise-secure - with zero data retention and strict tenant isolation, backed by SOC 2 Type 2 compliance.

For allocators, that means being able to track changes in a manager’s responses year over year. For GPs, it means consistent, data-backed answers across RFPs, DDQs, and consultant databases, without the manual rework. The results are measurable: AI-driven tools like Sidekick can reduce manual due diligence and reporting work by up to 40%, freeing up time for higher-value analysis and strategic work.

A Booming Market for Data-Driven AI

The market context is compelling: AI in asset management is projected to grow at a CAGR of 26.92% from 2025 to 2032, largely driven by growing data volumes and the pressing need for efficiency, compliance, and transparency. With 80% of wealth managers believing AI will revolutionize portfolio management and risk assessment in 2025, robust data structures have become a critical foundation for lasting value. However, 72% of firms still cite challenges with integrating AI beyond back-office operations, highlighting the necessity of tools that prioritize structured intelligence from the outset.

Conclusion

AI-first solutions might provide short-term efficiency. But without a structured foundation, they don’t scale, and they certainly don’t deliver the transparency or trust required in institutional investing.

Dasseti Sidekick was designed differently. By putting data first, it not only accelerates workflows but also strengthens institutional memory, giving firms a competitive edge today and into the future.