Why a PE Firm is Building AI Agents for its Portfolio Companies

Private equity was founded on the simple but powerful premise that active and thoughtful corporate ownership creates value. The original thesis and core obligation of the asset class then, is not to merely allocate capital but to strive to make companies fundamentally better.

Therefore, the ability to manage and operate portfolio companies is as much of a core competency for private equity firms as the ability to source, analyze, and execute investments.

Is there a pathway, then, for relatively smaller Korean private equity firms to develop meaningful competitive advantages against global competitors with larger resources and operations platforms?

We find that there are three distinct areas in which competitive advantages can be achieved by smaller or more localized investment firms: (1) through a more intimate, ground-level understanding of SME realities, (2) direct and real-time access to local operational challenges, and (3) the ability to rapidly execute solutions through a Minimum Viable Product (MVP) driven, agile approach.

It was this philosophy that led Centroid’s Operations Division to directly embed AI capabilities into the day-to-day workflows of its portfolio companies. The goal was to go beyond a mere proof-of-concept demonstration into deploying a working, practical AI agent that could help streamline actual operational bottlenecks and accelerate investment-related decision-making as well.

KAF (formerly Kolon Fiber) became the first company to deploy this in practice. KAF is a manufacturer of specialty short fibers that has been recognized by the Korean Ministry of Trade as a globally leading product manufacturer. The nature of the company’s high-specification products and complex production processes have required it to manage a vast and intricately interconnected web of operational data.

The Challenge at KAF

KAF's operations teams had long depended on Excel spreadsheets and internal information systems to manage production, quality, and sales data. While there was a large volume of data being generated on a daily basis, utilizing that information to produce meaningful insights remained a slow and largely manual process.

For example, in order to trace the source of quality deviations during a specific period, employees had to manually cross-reference a wide range of data (including processing conditions, lot-level data, shipment histories, etc.) that was stored under multiple Excel files, with the analysis often taking at least half a day to carry out.

More worryingly, the manual and non-standardized nature of the analysis process meant that personnel changes could lead to loss of expertise or even changes to the way information was being interpreted. This created challenges for both company management and Centroid, as it was difficult to maintain a consistent and reproducible monitoring process.

The Solution: A Multi-Agent AI System

Centroid decided to implement a role-based "multi-agent" architecture to solve this issue. Structured to mirror the way a real-world data analysis team would function, specialized agents were deployed in a way that would preserve efficiency while enabling cross-functional collaboration. This approach delivered three concrete outcomes:

1. Automated Analysis: Operational staff can now query data in natural language instead of needing to write formulas or directly manage data sets. The AI platform manages the entire process, from analytical design to visualization, reducing what previously took hours to a matter of moments. As a result, overall time spent on analysis and reporting has been reduced by more than 90%.

2. Human-in-the-Loop Reliability: In manufacturing, reliability is non-negotiable. The AI platform strictly functions as an analytical assistant, with final decision-making powers retained by domain experts that are given a fully transparent view of the underlying logic and data. This combination of AI-driven precision analysis and human oversight has translated into measurable improvements, including reduced variance in product quality and lower waste ratios driven by higher prediction accuracy.

3. Integrated Reporting: The impact also extends beyond the factory floor. On-site analysis and reports are now connected in real-time to Centroid’s monitoring infrastructure, allowing quality issues or key operational risks to be shared in a standardized format on an ongoing basis. The result has been faster and more consistent portfolio management – which the firm characterizes as a source of "Operational Alpha."

Conclusion: Centroid's AI Operations Philosophy

Centroid's approach to AI is not merely a technological initiative – it is built on the firm’s operational philosophy that investment returns are built through on-the-ground improvements at the portfolio level.

• Looking to the field for answers: Manufacturing operations generate a continuous stream of data. AI can be leveraged to structure this raw material into valuable insights that empower more precise decision-making.

• Structural transformation begins at the process level: In order to fundamentally improve a company’s profitability, we need to be able to fully understand and solve bottlenecks at the most granular processes. This is turn is achieved by empowering data-driven decision-making.

• Operational gains translate to investment performance: Stabilizing product quality, lowering defect rates, and enabling faster decision cycles are all factors that drive fundamental improvements to a company’s profitability. AI is not simply a cost-reduction tool, but a powerful engine that can drive value creation and multiple expansion.