Transforming Private Equity Workflows with Deterministic, AI-Enabled Modeling: Mosaic and Argonaut

Background

Argonaut is one of the largest and most successful PE firms based in the Central U.S. Over the firm’s 20+ year history, their focus on high-growth, diversified industrial companies (i.e., manufacturing, infrastructure, and industrial services) and modest use of leverage have made them partners of choice for entrepreneur- and family-owned businesses in Middle America. 

Before integrating Mosaic into the new deal review process, Argonaut’s lean and focused deal team reserved comprehensive valuation analysis for new opportunities to later stages of deal screening (as is necessary for any PE firm only using Excel). The firm’s high bar for quality of LBO analysis meant it would take 1-2 days to screen a new deal from a valuation perspective manually in Excel, excluding review and iteration. This created a bottleneck, limiting the team’s ability to quickly conduct thorough valuation analysis at the “top of the funnel” for new deal evaluation. 

Argonaut needed a faster way to screen new deals from a valuation perspective without compromising their high-quality bar. They sought a way for their juniors to spend less time building undifferentiated model mechanics and their mid-level to spend less time reviewing rote math. Instead, they wanted their investment professionals focused on what matters most: building conviction on the key assumptions underpinning those models.

‍

Our Approach

After adopting Mosaic, Argonaut quickly incorporated the platform into their core new deal review workflow, standardizing how opportunities are evaluated at the top of the funnel. Instead of building models from scratch in Excel – or pulling from a past deal template – the deal team now rapidly stands up high-quality LBO analyses tailored to the firm’s standard assumptions and deal structures, enabling a standardized, repeatable, and comparable evaluation across each opportunity reviewed by the firm.

The Mosaic team partnered closely with Argonaut throughout onboarding, providing hands-on support and ensuring the platform mapped to the firm’s investment approach. This collaboration extended beyond implementation, with ongoing feedback loops informing product development.

A clear example of this partnership is the introduction of Mosaic’s “Earnouts” special situation. In response to Argonaut’s product feature request, Mosaic built dedicated functionality to model performance-based management incentives and contingent payouts. This allowed the team to seamlessly incorporate more complex deal structures into their analyses. What began as a targeted solution for Argonaut has since been rolled out across Mosaic’s platform, demonstrating how customer feedback drives continuous product improvement for all users.

‍

Impact

With Mosaic, Argonaut has accelerated the speed and consistency of its deal evaluation process, enabling the team to filter opportunities more efficiently at the top of the funnel. The deal team can now focus their time on pressure-testing assumptions, exploring scenarios, and refining investment theses, rather than building, rebuilding, and endlessly checking rote model mechanics.

Argonaut is extremely selective with respect to the firms it chooses to partner with. The firm makes 2-4 new investments per year – but evaluates 300-400 prospective investments each year to find those rare opportunities. What used to take 1-2 days in Excel now takes ~10 minutes to set-up. This does not quantify any of the time saved in reviewing and re-reviewing models between each structural change, case addition, or material model update. All this time savings is repurposed in Argonaut’s case to commercial due diligence, and thoughtful debate around the deal model’s critical assumptions, inputs, and structures supporting a successful partnership with their prospective partners

This shift has not only improved efficiency but also elevated the quality of decision-making. By standardizing outputs and reducing manual work, Mosaic ensures that insights are both comparable across deals and easier to communicate internally.

Today, Mosaic supports the vast majority of Argonaut’s use cases, and in some instances, has even been used to generate final investment committee (IC) materials, highlighting its reliability not just for screening, but for decision-making at the highest level.