Cotality Joins Snowflake’s Open Semantic Interchange Initiative to Advance Agentic AI Standards in Financial Services
As artificial intelligence continues to reshape the financial services industry, one of the greatest challenges facing organizations is not simply accessing data but ensuring that data can be consistently understood, interpreted, and acted upon by AI systems. Recognizing the growing need for standardized, interoperable data frameworks, Cotality™, a leading provider of global property information, analytics, and data-enabled solutions, has announced its participation as a founding member of Snowflake’s Financial Services Working Group under the Open Semantic Interchange (OSI) initiative.
The announcement, made during Snowflake Summit 2026, highlights a significant step toward establishing industry-wide standards that will enable the next generation of agentic artificial intelligence across financial services. Through its involvement in the working group, Cotality will help develop the semantic structures, definitions, and frameworks required to transform complex property and financial data into AI-ready assets that can be used seamlessly across platforms and ecosystems.
The collaboration represents more than a technology partnership. It reflects a broader industry effort to address one of the most critical barriers to enterprise AI adoption: the challenge of creating a common language that enables data, applications, and AI agents to interact effectively without extensive manual intervention.
The Growing Importance of Agentic AI in Financial Services
Artificial intelligence has already become an important tool across banking, insurance, lending, investment management, and real estate markets. Organizations use AI to automate workflows, analyze risk, detect fraud, improve customer experiences, and support decision-making.
However, the next phase of AI evolution is expected to move beyond predictive analytics and conversational assistants toward agentic AI systems—autonomous agents capable of performing complex tasks, making decisions, and executing workflows with minimal human involvement.
These AI agents have the potential to transform financial services by streamlining underwriting processes, improving credit risk assessments, accelerating claims management, enhancing portfolio analysis, and supporting a wide range of operational activities.
Yet the effectiveness of these systems depends heavily on data quality and consistency.
For AI agents to function reliably, they must understand the meaning and context of data from multiple sources. This requires more than simply connecting datasets. It requires standardized semantic frameworks that ensure information is interpreted consistently across organizations, platforms, and applications.
Without these standards, AI systems face significant limitations when attempting to operate autonomously in complex financial environments.
Addressing the Challenge of Semantic Friction
One of the key issues facing enterprises today is what industry experts refer to as “semantic friction.”
Semantic friction occurs when data from different sources uses varying structures, definitions, formats, and terminologies. Even when organizations possess vast amounts of valuable information, inconsistencies in how that information is organized can make it difficult for AI systems to understand and utilize effectively.
As companies seek to deploy large language models and autonomous AI agents at scale, data teams often spend considerable time translating, cleansing, mapping, and restructuring information before it can be used.
These processes increase costs, slow innovation, and limit the scalability of AI initiatives.
Cotality and Snowflake believe that addressing semantic friction is essential for unlocking the full potential of enterprise AI.
By establishing common semantic standards through the Open Semantic Interchange initiative, organizations can create data environments where information is inherently understandable by AI systems without requiring extensive manual transformation.
This capability is expected to significantly accelerate the adoption of agentic AI throughout the financial services ecosystem.
What Is Open Semantic Interchange?
Open Semantic Interchange, or OSI, is an initiative designed to establish a shared language for enterprise data ecosystems.
The framework aims to create standardized definitions and semantic models that allow organizations to exchange and interpret information consistently across cloud platforms, software applications, and AI systems.
Rather than focusing solely on technical connectivity, OSI addresses the meaning behind data.
For example, financial institutions often use different terminologies, structures, and metadata standards when describing customers, properties, loans, insurance policies, or investment assets.
Even when the underlying information is similar, differences in representation can create barriers to interoperability.
OSI seeks to eliminate these barriers by providing a common semantic foundation that enables systems to understand information in a consistent and reliable manner.
The result is greater interoperability, improved automation, and more effective AI deployment across industries.
Cotality’s Role as a Founding Member
As a founding participant in the Financial Services Working Group, Cotality will contribute its deep expertise in property intelligence, analytics, and financial data.
The company possesses extensive datasets covering real estate markets, property characteristics, mortgage activity, valuation trends, risk indicators, and related financial information. These datasets play an important role in lending, insurance, investment analysis, and housing market assessments.
Through the OSI initiative, Cotality will help establish the structural definitions and semantic frameworks needed to make this information more accessible and actionable for AI-driven systems.
The goal is to ensure that property and financial data can be seamlessly integrated into agentic workflows without requiring extensive customization or data engineering.
By aligning its data assets with OSI standards, Cotality aims to create an interoperable data layer capable of supporting autonomous decision-making across multiple financial services use cases.
This work is particularly important because property data often intersects with numerous financial processes, including mortgage underwriting, property insurance, asset valuation, risk management, and investment decision-making.
Creating AI-Ready Data at Scale
Cotality’s involvement in the initiative aligns with its broader strategy of becoming an AI-first data organization.
Earlier in 2026, the company introduced its native AI-Ready Data architecture, designed to optimize data assets for artificial intelligence applications.
The architecture focuses on ensuring that datasets are structured, standardized, and enriched with the contextual information needed for machine learning models and AI agents to operate effectively.
Participation in the Open Semantic Interchange initiative extends this strategy by moving beyond AI readiness toward semantic interoperability.
Instead of merely making data available to AI systems, Cotality is helping define how that data should be interpreted and understood across the industry.
This distinction is significant because successful AI adoption increasingly depends not only on data access but also on shared understanding.
Organizations that can provide semantically enriched, interoperable data are likely to play a critical role in the emerging AI ecosystem.
Supporting Financial Services Transformation
The collaboration is expected to deliver meaningful benefits across several segments of the financial services industry.
Insurance
Insurance companies rely heavily on property intelligence and risk data to assess exposure, price policies, and manage claims.
By standardizing semantic frameworks, insurers can improve interoperability between underwriting systems, claims platforms, and AI-driven risk assessment tools.
This may lead to faster decision-making, improved accuracy, and more efficient operations.
Lending
Mortgage lenders and financial institutions depend on accurate property data when evaluating borrowers and collateral.
AI agents equipped with semantically consistent information can streamline loan origination, underwriting, and portfolio management processes while reducing operational complexity.
Capital Markets
Investment firms increasingly use alternative data sources to support asset valuation, market analysis, and investment strategies.
Standardized semantic structures can help integrate diverse datasets more effectively, enabling AI systems to generate insights with greater confidence and consistency.
Enterprise Risk Management
Financial institutions face growing regulatory and operational requirements related to risk management.
Agentic AI systems powered by standardized data frameworks can improve monitoring, reporting, and scenario analysis while supporting governance and compliance objectives.
Building Trustworthy and Auditable AI Systems
One of the most important goals of the initiative is to support the development of trustworthy AI.
As AI systems take on more responsibility for decision-making, organizations must ensure that outcomes are transparent, explainable, and auditable.
Poorly structured data increases the risk of errors, inconsistencies, and AI hallucinations.
Semantic standards help mitigate these risks by providing clear definitions and contextual information that guide AI behavior.
According to Cotality, establishing reliable semantic foundations is essential for enabling autonomous systems to operate with greater accuracy and accountability.
This is particularly important in regulated industries such as financial services, where decisions related to lending, insurance, investments, and risk management often carry significant legal and economic consequences.
By supporting transparent and auditable AI processes, the initiative aims to build confidence among regulators, enterprises, and end users.
Differentiating Through Open Standards Leadership
The collaboration also highlights a strategic distinction between companies that simply incorporate AI features and those helping shape the underlying infrastructure that powers AI ecosystems.
Many technology vendors are focused on adding generative AI capabilities to existing products. While these enhancements can provide value, they do not necessarily address the foundational challenges associated with data interoperability and semantic consistency.
Cotality’s role within the Financial Services Working Group positions the company as a contributor to the underlying standards that enable scalable AI automation.
By participating in the development of universal data definitions and semantic frameworks, the company seeks to establish itself as a key infrastructure provider for the future AI economy.
This approach reflects a long-term vision in which open standards become a critical enabler of innovation across financial services.
Snowflake’s Vision for an Interoperable AI Ecosystem
Snowflake views the Open Semantic Interchange initiative as an important step toward creating more connected and intelligent enterprise ecosystems.
As organizations increasingly deploy AI across multiple platforms and environments, interoperability becomes essential for maximizing value and minimizing complexity.
The company believes that common semantic standards can serve as the foundation for secure, scalable, and trusted AI automation.
Industry-specific expertise from founding members such as Cotality plays an important role in achieving this vision.
By contributing deep knowledge of property and financial data, Cotality helps ensure that the resulting standards address real-world business requirements and support practical enterprise applications.
The partnership between Cotality and Snowflake represents a significant development in the evolution of enterprise AI.
As organizations move toward increasingly autonomous systems, the importance of standardized, semantically rich data will continue to grow.
Agentic AI has the potential to transform financial services by automating complex workflows, improving decision-making, and unlocking new levels of efficiency. However, realizing this potential requires a strong foundation built on interoperability, transparency, and shared understanding.
Through its role in the Open Semantic Interchange Financial Services Working Group, Cotality is helping create that foundation.
By addressing semantic friction, promoting open standards, and enabling AI-ready data at scale, the company is contributing to a future where intelligent agents can operate more effectively across the financial ecosystem. As the adoption of autonomous AI accelerates, initiatives like OSI may prove instrumental in shaping the next generation of financial services innovation.
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