How Alkymi uses agentic AI to automate private markets workflows

In private markets, workflow automation has often hit a wall when traditional AI systems encounter unstructured data or unexpected document variations. While machine learning can handle straightforward tasks like processing capital calls or structured documents such as financial statements, more complex documents like private credit agreements have remained beyond its reach.

At Alkymi, we've embraced agentic AI to overcome these challenges. By enabling our systems to make dynamic, context-aware decisions, we’re breaking through these barriers and automating the most complex workflows, empowering firms to transform their toughest data challenges into opportunities for innovation.

Static workflow automation: Rule-based and machine learning solutions

Current document automation systems rely on static processes that follow specific steps in a predefined sequence. Rule-based systems are effective for documents with consistent formats. For example, a system might always search for an account number in the top-right corner of a brokerage statement from a particular broker. However, these static systems are brittle—any variation in layout or structure may cause them to fail, requiring frequent manual updates to their rules.

Platforms that incorporate machine learning address some of these limitations by using models trained on labeled examples to make predictions. This provides greater flexibility, as machine learning can generalize across a wider range of document formats without constant rule modifications. However, even these systems follow rigid sequences of steps—such as searching, extracting, and validating data—without the ability to adapt dynamically to the document's unique structure. Moreover, their performance heavily depends on the quality and quantity of labeled training data, which can be costly and time-consuming to collect.

Agentic AI: A new paradigm for data processing

The document automation systems described above are fundamentally limited by their reliance on static processes. They struggle to adapt to unstructured or highly variable documents, leaving many complex workflows out of reach. Agentic AI addresses this gap by introducing true adaptability into data workflows, enabling systems to dynamically respond to the unique context of each document.

At the core of agentic AI is an “agent,” a decision-making component that continuously evaluates its current state, objectives, and the specific requirements of a task. Instead of following a rigid, predefined sequence, the agent dynamically selects from a suite of modular tools—such as search, data extraction, calculation, and validation—to craft a workflow tailored to each document.

This flexibility allows agentic AI to excel where traditional systems fall short, handling even the most complex and unstructured documents with ease. By combining advanced machine learning with a dynamic, goal-driven framework, agentic AI represents a transformative leap in document automation, making it possible to solve challenges that static systems simply cannot address.

Example: Processing private credit agreements

Consider a private credit agreement, a dense, 500-page document laden with legal language. To determine the maturity date for a loan, traditional systems might extract the following text:

“Maturity Date” shall mean the date that is the seventh anniversary of the Closing Date, or, if such date is not a Business Day, the immediately preceding Business Day.

While this information is relevant, it isn’t a date that can be stored in a database. A developer would need to write code to extract additional information from the document (e.g. the Closing Date) and more logic to synthesize the results into a final answer. This approach is brittle—what works for one credit agreement might fail for others due to differences in formatting or structure.

Alkymi solves these challenges with an agent equipped with a set of tools capable of performing a wide range of data processing tasks. These tools include:

• A search function to locate specific terms and phrases
• A date calculator for performing temporal computations
• A calendar tool to verify and adjust dates based on business days

When presented with a new credit agreement, the agent dynamically decides which tools to deploy and in what order, tailoring its workflow to the specific structure and content of the document. For example, it might first use the search tool to find the closing date, then pass this information to the date calculator to compute the maturity date, and finally validate the result using the calendar tool. This adaptive, goal-driven approach ensures that Alkymi’s agentic AI can process not only a single document type but a diverse array of variations, without requiring custom programming for each case.

Putting agents to work

At Alkymi, we’re already putting agentic AI into action, embedding it within our Patterns to automate some of the most challenging workflows in private markets. Today, our agents dynamically adapt to diverse document types, selecting the right tools and strategies in real time to extract, process, and validate data with precision. This approach has unlocked new possibilities for automation, enabling us to tackle workflows that were previously impossible to automate and to generate greater insights from private markets data.

Looking ahead, we see agents playing an even greater role in reshaping private markets. As this technology evolves, we’re building systems that not only adapt to the complexities of individual documents but also learn and improve over time, creating intelligent collaborators that redefine efficiency and scalability in data processing. By continuing to innovate, we aim to empower financial services firms with tools that transform their operations and set a new benchmark for what insights are achievable in private markets.