Accelerate your agentic future with AI-ready data and building blocks

Agentic AI is supposed to change everything — yet most enterprises today can’t get past the starting line. The vision is clear; the roadmap isn’t. In a widely shared State of AI in Business report, MIT researchers found that many organizations are getting “zero return on their GenAI investments.” While most have explored standard tools like ChatGPT, Gemini, and Copilot to enhance employee productivity, very few have taken the next step: developing their own custom AI agents and wiring them directly into their business processes.

Much of the hold-up comes from building agentic AI from scratch — meaning long timelines, heavy infrastructure, and big development budgets. But there’s a faster, safer, and ultimately far more cost-effective approach: strategic partnerships.

That same MIT report found that “pilots built via strategic partnerships are 2x as likely to reach full deployment as those built internally.” Instead of going at it alone, trailblazers can team up with outside partners (like Cotality) and major data and AI platforms (like Databricks, Google Cloud, and Snowflake) that understand their needs and have the AI building blocks required to get a jumpstart on their AI ambitions.

By using agentic capabilities from their existing data and AI platform, and AI-ready assets from Cotality, forward-thinking mortgage lenders, real estate brokers, and other users of property, location and homeownership data can skip the false starts and get a clear head start over their competitors.

Why do you need AI agents in the first place?

Agentic AI is unlocking clear value across industries that depend on property and location intelligence. Early use cases share a common theme: teams need fast, reliable insight from their data.

Real estate, brokers, homebuilders, and proptech companies want quick access to property, neighborhood, valuation, and climate intelligence to enrich CRMs, guide pricing, support clients, and plan development. These decisions slow down when data lives across incompatible systems and legacy analytics tools.

The mortgage industry faces similar challenges. Lenders need to improve marketing ROI, understand lost leads, benchmark loan officer performance, and proactively manage portfolio risk using market and buying trends. Cotality’s recently released Payoff Analysis Agent on Google Cloud’s Gemini Enterprise is a good example of how agentic AI can instantly surface these insights once the underlying data is connected and AI-ready.

Beyond real estate and mortgage, industries like insurance, telecom, and retail depend on trusted property and location insights for everything from lead generation and customer retention to capacity planning and site selection. Across all of them, teams urgently need streamlined data and access to AI-ready data that integrates directly into their day-to-day workflows.

Agentic AI bridges these gaps when the right data foundation is in place – connecting disparate data sources, automating multi-step analysis, and delivering clear answers instantly so teams can make better decisions with confidence.

Why AI-ready data matters

Data is the backbone of any AI strategy, and better data invariably yields better outcomes.

Agentic AI has the potential to shrink decision cycles from weeks to minutes, uncover portfolio risk before it materializes, and transform compliance from a manual burden into a continuous, intelligent process, but none of that potential can be realized if the data is all over the place, poorly structured, or out of context. Even the best AI agent misses the mark without the right data beneath it.

That’s where Cotality comes in. We collect data from more than 22,000 data sources and process real-time signals for 99.9% of US properties. This includes comprehensive property and transaction details, aerial and LiDAR imagery, appraisals, insurance premiums, complete mortgage data, voluntary and involuntary liens, hazard risks, and even detailed neighborhood data by census tract.

Trusted data is everything. We clean it, cross-check it, format it, and add relevant knowledge graphs and semantic layers to ensure reliable outputs when leveraged in today’s top cloud AI and data platforms: Snowflake Intelligence and its newly launched Cortex AI for Financial Services, Google Cloud’s Gemini Enterprise, or Databrick’s AI/BI Genie.

Well-structured and contextualized for today’s diverse use cases, AI-ready data is essential to guarantee the accuracy and relevancy of AI-driven insights. But it’s not the only piece of the puzzle.

The power of AI-ready assets

Even with AI-ready data powering your AI platforms, interpreting that data and uncovering hidden patterns can be time-consuming. Prebuilt AI-ready assets (specialized components like reusable frameworks, apps, and AI agents) can handle the analytical heavy lifting and help you quickly turn your data into actionable insights. The payoff is faster time to value, fewer engineering headaches, and the freedom to focus on strategy rather than data analysis.

Take Cotality’s CLIP App for Snowflake AI Data Cloud, for example, or the CLIP MCP for Databricks Model Context Protocol (MCP). They can be called by other agents or modules on their respective platforms to leverage the CLIP ID, a unique property identifier developed by Cotality to connect all records related to a property across data sources (just like a social security number or VIN, but for real estate). With the CLIP App and CLIP MCP, users can instantly match and append the CLIP ID to their data to deduplicate, enrich, and unify their property data across their portfolio — leading to a clearer, single source of truth for analysis.

Best of all, these AI-ready assets and agents, like our Payoff Analysis Agent on Google Cloud, and processes occur directly within your cloud environment, ensuring your data assets remain secure.

The shift toward an agentic future doesn’t require a total rebuild. By tapping into AI-ready data, prebuilt assets like the CLIP App, and turnkey agents like Payoff Analysis Agent, you can solve critical use cases immediately. Talking to the data — and getting the right answer every time, safely, and instantly — is finally within reach.