# Private Equity Case Study: From Possibility to Measurable Impact | Syntari

Source: https://syntari-ai.com/insights/case-studies-and-white-papers/private-equity

CASE STUDIES · PRIVATE EQUITY
CASE STUDY: PRIVATE EQUITY
From Possibility to Measurable Impact
Tools don’t create advantage. Systems do. When AI is designed around human intelligence, clear operating models, and measurable outcomes, it becomes a force multiplier, not a liability. That’s the difference Syntari delivers.
A global private equity firm with over $22B in assets under management and a lean, highly analytical deal team operating across complex credit and special situations. The firm evaluates a high volume of opportunities under tight time constraints, where speed, accuracy, and trust in analysis are critical to investment decision-making.
The firm set out to build an AI-enabled deal automation capability not as a standalone tool, but as a durable operating system embedded into how its investment professionals work.
Industry Private Equity Size 200 employees Founded 2001 “The goal was never to automate thinking. It was to give the team a faster, more reliable starting point,one they could trust, interrogate, and build on.”
Challenge
The firm’s deal teams were spending significant time manually compiling and synthesizing information from fragmented sources, including teaser emails, CIMs, attachments, internal notes, and third-party data providers. Critical early-stage workflows, particularly the creation of investment “quick cuts” and one-pagers, were slow, inconsistent, and heavily dependent on individual analysts.
AI experimentation had shown promise, but early efforts surfaced a key tension: speed alone did not equal trust. Analysts needed transparency into sources, the ability to override outputs, and confidence that AI-supported analysis would hold up under investment committee scrutiny.
As the firm articulated internally, the goal was not full automation. It was to reduce time-to-decision, surface signal earlier, and institutionalize repeatability without disrupting analyst judgment.
Solution
Syntari partnered with the firm to design and implement an AI-native deal automation workflow anchored in real investment practices, not abstract tooling.
Over a structured 12-week pilot, Syntari delivered:
An end-to-end “Movers → One-Pager” AI workflow embedded into existing deal team processes
Automated extraction of financial, narrative, and capital structure signals from unstructured materials
A standardized, Excel-based investment one-pager aligned to how analysts already work
Human-in-the-loop controls, including source traceability and analyst override capability
A deliberate change management and adoption approach to build confidence, not resistance
Rather than replacing analysts, the system was designed to act as a copilot, accelerating first drafts, standardizing structure, and freeing teams to focus on judgment and decision-making.
As Syntari Founder & CEO Rafi Menachem described the approach:
“The goal was never to automate thinking. It was to give the team a faster, more reliable starting point,one they could trust, interrogate, and build on.”
Result
Within the pilot period, the firm confirmed measurable gains across speed, quality, and adoption.
Confirmed Outcomes
75% faster delivery velocity for early-stage deal materials
50–75% reduction in preparation time for investment one-pagers
65% of the deal team advanced to AI-enabled workflows, with adoption continuing to grow
15% improvement in project margins, driven by efficiency and reuse
Reusable delivery playbooks created for future deal teams and use cases
In addition, the firm documented a critical qualitative shift: analysts reported higher confidence in outputs once transparency and override controls were introduced, validating the importance of human-in-the-loop design.
Before Syntari, deal teams were constrained by manual effort, inconsistent formats, and limited reuse. After implementation, the firm gained:
Near-instant creation of standardized deal summaries
Consistent structure across investment materials
AI-supported workflows available 24/7 with unlimited capacity
A foundation for expanding AI into diligence, portfolio operations, and governance
“Early MVPs proved automation could be significantly faster, but trust came from transparency and analyst control. Once that was in place, adoption followed.”
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