In our conversations with commercial real estate firms, one question comes up again and again: Why use a specialized AI platform like Keyway instead of ChatGPT, Claude, or even building something in-house?
It's a fair question. General-purpose AI models have become remarkably capable of analyzing documents, extracting information, and answering complex questions. And as access to these capabilities becomes more widespread, the value companies derive from AI increasingly depends on how effectively they put it to work.
For commercial real estate firms, that means connecting AI to their own documents, data, internal standards, and existing workflows. The opportunity extends beyond completing individual tasks faster to making entire processes more efficient, consistent, and reliable.
That's where AI built specifically for CRE delivers its value.
Commercial real estate has its own terminology, financial frameworks, document structures, and relationships. Every firm also operates with its own underwriting assumptions, reporting requirements, approval processes, and institutional knowledge.
This is where the Keyway Harness comes in. It is the infrastructure surrounding the underlying AI models, connecting a firm's documents, data, institutional knowledge, and workflows with the validation and controls required for real-world CRE operations. Rather than relying on the model alone, Keyway provides the industry-specific context and operational structure needed to turn its capabilities into consistent, reviewable results.
A general-purpose AI model can analyze a lease or summarize an offering memorandum. But producing an accurate response to an individual request is only one part of what CRE teams need to accomplish.
Consider a lender evaluating a financing opportunity. Relevant information may be spread across an offering memorandum, rent roll, borrower financials, operating statements, leases, and internal credit standards.
The team needs to extract relevant information, reconcile figures across documents, identify inconsistencies, and prepare materials for credit review. Each step depends on the others, and the results must be traceable to their original sources.
Keyway brings those documents, data, and processes into a connected environment where AI can support the workflow from initial intake through analysis and review.
The same principle applies across the entire commercial real estate life cycle, from acquisitions and underwriting to asset management and lease administration.
The value comes from applying AI within the context of how CRE professionals actually work.
Access to powerful AI models is increasingly widespread. What differentiates the value organizations get from them is how effectively that intelligence connects to their proprietary information, internal standards, and operational processes.
For CRE firms, this distinction becomes particularly important when AI moves beyond occasional use and becomes part of recurring workflows.
An acquisitions team may need to identify properties, build comparable sets, validate pricing, and prioritize opportunities. An asset manager may need to review hundreds of leases, reconcile amendments, and prepare information for accounting and reporting. A lender may need to turn incoming deal documents into a source-backed loan narrative.
These processes require more than extracting information. They depend on consistent data structures, relationships between documents, validation rules, and outputs that fit the organization's existing requirements.
Keyway is designed around these needs, allowing teams to work with their own information while maintaining visibility into the sources and reasoning behind AI-generated results.
For InterCapital Group, a multifamily investment firm, this means using Keyway to generate customized comparable sets based on its investment criteria, bringing relevant property and market information into the underwriting process. The firm has reported saving more than 20 hours per deal and shortening property evaluation timelines by up to two weeks.
The significance extends beyond completing individual analyses faster. By reducing the manual work required to prepare and evaluate information, the team can dedicate more attention to assessing additional opportunities and applying its investment expertise on more deals.
In commercial real estate, AI-generated information frequently supports decisions with significant financial consequences.
A discrepancy in a rent roll, an incorrectly interpreted lease provision, or an unsupported assumption in an underwriting analysis can affect how a transaction is evaluated.
That makes the ability to verify information as important as the speed at which it is produced.
CRE professionals need to understand where figures originated, identify what requires additional review, and retain control over the final output.
Keyway supports this process through source-linked information, discrepancy identification, and workflows that allow users to review and validate AI-generated results.
This becomes particularly valuable when multiple documents contain related or conflicting information.
Consider a lease amendment that changes the expiration date and introduces a rent concession while leaving operating expense obligations unchanged. An accurate abstraction needs to account for the original lease and its amendments together, identify which provisions remain in effect, and make the supporting language available for review.
The goal is to give professionals reliable information they can evaluate and use with confidence.
As AI becomes more involved in complex workflows, that combination of efficiency, transparency, and human oversight becomes increasingly important.
As general-purpose AI becomes more capable, some CRE firms may consider building their own solutions.
That can be a viable approach, particularly for organizations with substantial technical resources and highly specific requirements.
But the cost of implementing AI extends beyond model access.
Organizations need to connect their documents and systems, structure information, establish validation processes, manage permissions, support users, and maintain workflows as their business requirements evolve.
A successful AI demonstration may show what a model can accomplish with a particular document. Deploying that capability consistently across hundreds or thousands of documents requires additional infrastructure, testing, integration, and ongoing support.
Consider lease abstraction.
One large asset manager is working with Keyway to create structured lease abstracts in minutes rather than weeks, trace information to its source, and generate outputs formatted for its existing Yardi schema. The process has reduced manual lease-review time by 90% and is saving more than $100,000 annually in direct costs.
A general-purpose model can extract and summarize lease terms. The challenge becomes more complex when those results need to be validated across a portfolio, mapped to a company's accounting conventions, and delivered consistently into its existing systems.
At that point, the value depends on the infrastructure surrounding the model and its ability to turn individual outputs into a reliable business process.
For CRE firms evaluating whether to build or buy, the relevant comparison includes the resources required to develop, deploy, maintain, and improve those processes over time.
The most meaningful measure of AI's impact is ultimately what an organization can accomplish with it.
Faster document processing, more consistent analysis, and easier access to information matter because they allow professionals to spend more time evaluating opportunities, managing risk, and making informed decisions.
For commercial real estate firms, achieving those outcomes requires AI that works with the information they already have, understands the requirements of their business, and fits into the processes their teams rely on.
That is the value Keyway is designed to deliver.
As access to advanced AI continues to expand, the advantage will increasingly come from how effectively companies connect their data, expertise, and workflows to put that technology to work.
The real value of AI in commercial real estate lies in what an organization can reliably accomplish with it, using its own data, expertise, and workflows.
Vertical AI combines powerful AI models with commercial real estate-specific data, context, workflows, data structures, and validation.Instead of simply responding to individual prompts, it is designed to work within the processes and requirements of CRE organizations.
General-purpose AI can perform tasks such as summarizing documents, extracting information, and answering questions. CRE workflows often require additional context, source traceability, validation, integrations, and an understanding of how information relates across documents, assets, deals, and systems.
The answer depends on a firm’s resources and requirements. The comparison should account not only for model costs, but also for the engineering, integrations, workflow development, validation, testing, monitoring, and ongoing maintenance required to operate AI reliably.
