Cohesion: AI Agents for Smarter Equity Research

In an era where financial markets move faster than ever, the ability to process information efficiently has become one of the most valuable competitive advantages. Cohesion, a startup founded in 2026, positions itself at the center of this transformation by introducing a new category of tools: AI-powered agentic teammates for public equities investing.

Rather than functioning as a traditional analytics platform, Cohesion operates as a system of autonomous agents designed to think, monitor, and surface insights on behalf of analysts. These agents are not passive tools waiting for queries—they actively scan, interpret, and connect information across a wide range of sources.

The company’s mission is straightforward yet ambitious: to eliminate inefficiencies in how investment professionals gather and process data. In doing so, Cohesion aims to fundamentally change how hedge funds and equity analysts approach research, decision-making, and ultimately, alpha generation.

What Problem Is Cohesion Trying to Solve?

The modern investment landscape is drowning in data. Earnings calls, financial statements, news articles, podcasts, and social media platforms like X (formerly Twitter) generate an overwhelming volume of information daily. For analysts, the challenge is no longer access to data—it is the ability to extract meaningful signals from the noise.

Traditionally, analysts spend hours each week manually tracking these sources. They listen to earnings calls, read transcripts, monitor news updates, and scan social platforms for sentiment shifts. Despite this effort, important signals are often missed simply because no human can process everything in real time.

Existing tools have not kept up with this reality. Many rely on outdated workflows that require manual searches, keyword queries, and fragmented dashboards. These systems place the burden of discovery on the user, forcing analysts to ask the right questions before they can find answers.

Cohesion identifies this as a structural inefficiency in the market. The startup argues that the problem is not a lack of intelligence among analysts, but rather a lack of systems capable of augmenting that intelligence at scale.

How Does Cohesion’s Agentic System Work?

At the core of Cohesion’s product is a team of AI agents designed to function as an extension of an analyst’s workflow. These agents continuously monitor companies, sectors, and data streams without requiring manual intervention.

Unlike traditional software, which operates reactively, Cohesion’s agents are proactive. They track earnings releases, parse financial commentary, and analyze non-traditional datasets such as podcasts and social media discussions. By doing so, they uncover insights that might otherwise remain hidden.

The concept of “agentic” systems is key to understanding Cohesion’s approach. Each agent is capable of performing specific tasks autonomously—whether it’s summarizing an earnings call, identifying sentiment trends, or flagging anomalies in market behavior. Together, these agents form a coordinated system that delivers actionable insights to users.

Importantly, the platform is designed to require minimal setup. Analysts do not need to configure complex workflows or define extensive parameters. Instead, the system integrates seamlessly into existing processes, allowing users to focus on decision-making rather than data collection.

Why Are Non-Traditional Data Sources So Important?

One of Cohesion’s defining features is its emphasis on non-traditional datasets. While financial statements and earnings reports remain critical, they no longer provide a complete picture of market dynamics.

Podcasts, for example, have become a significant channel for industry insights, executive commentary, and informal discussions that can influence investor sentiment. Similarly, platforms like X serve as real-time indicators of public perception, emerging narratives, and breaking developments.

Historically, these sources have been difficult to incorporate into investment workflows. They are unstructured, time-consuming to analyze, and often overlooked due to resource constraints.

Cohesion addresses this gap by enabling its agents to process and interpret these datasets alongside traditional financial information. This holistic approach allows analysts to identify patterns and correlations that would be nearly impossible to detect manually.

In doing so, the platform expands the scope of what constitutes “relevant data” in investment analysis, giving users a broader and more nuanced understanding of the market.

How Does Cohesion Improve Investment Decision-Making?

The ultimate goal of any investment tool is to improve decision-making. Cohesion achieves this by reducing the time spent on low-value tasks and increasing the availability of high-quality insights.

One of the most common challenges analysts face is understanding why a particular stock is moving. This often requires piecing together information from multiple sources, a process that can be both time-consuming and error-prone.

Cohesion’s agents streamline this process by automatically surfacing relevant signals and providing context for market movements. Instead of searching for answers, analysts receive curated insights that explain what is happening and why.

Additionally, the platform supports automated idea generation. By continuously monitoring data and identifying trends, the agents can highlight potential investment opportunities that align with a fund’s strategy.

This shift from manual research to augmented intelligence enables analysts to focus on higher-level thinking, such as evaluating investment theses and managing risk.

Who Are the Founders Behind Cohesion?

Cohesion’s founding team brings together a combination of financial expertise and technical experience, which is critical for building a product at the intersection of AI and investing.

Devon Krapcho, the CEO and co-founder, previously worked as a hedge fund analyst for over five years at Long Path Partners. His experience covering software companies provided firsthand insight into the challenges analysts face when managing large volumes of information.

Matthew McBrien, another co-founder, has a background in engineering and security, having worked at Amazon Web Services and Amazon. His expertise contributes to the platform’s technical robustness and scalability.

Matt Munns, the third co-founder, has experience building AI solutions for investors at T. Rowe Price. His background bridges the gap between advanced machine learning techniques and practical applications in finance.

Together, the team combines over a decade of experience in public markets investing, giving them a deep understanding of both the problem and the potential solutions.

What Traction Has Cohesion Achieved So Far?

Despite being a relatively new startup, Cohesion has already demonstrated significant traction. The platform is currently live with more than ten long/short and long-only fundamental equity funds.

These funds collectively manage over $10 billion in assets under management (AUM), indicating a high level of trust in the platform’s capabilities. Early adoption by professional investors suggests that Cohesion is addressing a real and pressing need in the market.

This traction also highlights the growing demand for AI-driven tools in finance. As competition intensifies, funds are increasingly looking for ways to gain an edge, whether through better data, faster insights, or more efficient workflows.

Cohesion’s ability to secure early users in this space is a strong signal of its potential to scale and expand its impact.

How Does Cohesion Compare to Traditional Tools?

Traditional investment tools are often built around static data and manual workflows. They require users to search for information, run queries, and interpret results independently.

While these tools can be powerful, they place a significant cognitive load on analysts. The process of gathering and synthesizing information becomes a bottleneck, limiting the speed and effectiveness of decision-making.

Cohesion takes a fundamentally different approach. By leveraging AI agents, the platform shifts from a user-driven model to a system-driven one. Instead of asking questions, analysts receive answers.

This paradigm shift has several advantages. It reduces the risk of missing critical information, improves efficiency, and enables a more proactive approach to investing.

However, it also raises new considerations, such as the need for transparency and trust in AI-generated insights. Cohesion’s success will depend not only on its technical capabilities but also on its ability to build confidence among users.

What Does the Future Hold for Agentic Investing?

The concept of agentic systems represents a broader trend in the evolution of software. As AI becomes more advanced, tools are moving from passive interfaces to active participants in workflows.

In the context of investing, this shift has the potential to redefine how decisions are made. Analysts may increasingly rely on AI agents to handle data collection, preliminary analysis, and even hypothesis generation.

Cohesion is at the forefront of this transition. By building a platform specifically designed for public equities, the company is exploring what it means to have an AI “teammate” rather than just a tool.

Looking ahead, the implications are significant. If agentic systems can consistently deliver high-quality insights, they could become a standard component of investment workflows across the industry.

At the same time, the human element will remain essential. Judgment, experience, and strategic thinking cannot be fully automated. Instead, the most successful investors will likely be those who can effectively combine human expertise with AI-driven capabilities.

Why Could Cohesion Redefine Market Efficiency?

Market efficiency has long been a central concept in finance, referring to the degree to which asset prices reflect all available information. In practice, however, inefficiencies persist due to limitations in how information is processed.

Cohesion’s vision is to reduce these inefficiencies by making it easier to access and interpret relevant data. By automating the analysis of both traditional and non-traditional sources, the platform helps ensure that important signals are not overlooked.

This could have a profound impact on the market. As more participants adopt similar tools, the speed and accuracy of information processing may increase, leading to more efficient pricing.

At the same time, it may also raise the bar for generating alpha. As basic inefficiencies are eliminated, investors will need to rely on deeper insights and more sophisticated strategies to outperform.

In this sense, Cohesion is not just building a product—it is contributing to a broader transformation in how financial markets operate.

Conclusion: Is Cohesion the Future of Equity Research?

Cohesion represents a compelling vision for the future of equity research. By combining AI agents with deep domain expertise, the startup addresses one of the most persistent challenges in investing: the ability to process information effectively.

Its agentic approach offers a new way of thinking about software—not as a tool to be used, but as a teammate to collaborate with. This shift has the potential to unlock significant productivity gains and improve decision-making across the industry.

While it is still early in its journey, Cohesion’s traction and founding team suggest a strong foundation for growth. As the platform evolves, it will be interesting to see how it shapes the workflows of analysts and the strategies of investment firms.

Ultimately, the success of Cohesion will depend on its ability to deliver consistent, reliable insights while earning the trust of its users. If it can achieve this, it may well become a defining player in the next generation of financial technology.