Cherre

Learning Objectives

• Understand how a data-integration and knowledge-graph platform becomes the foundation for real estate AI
• See what Agent.STUDIO lets firms build — agentic workflows and natural-language querying over connected data
• Recognize that the AI layer is only as good as the underlying connected data

What Is Cherre?

Cherre is a real estate data-management platform that connects, cleans, and links a firm's scattered property information into a single, queryable foundation. The concrete problem it solves is data fragmentation: a typical owner, lender, or investment manager keeps rent rolls, valuations, market feeds, lease data, and third-party datasets in dozens of disconnected systems, spreadsheets, and formats. Cherre ingests those sources, resolves them against a universal and semantic data model, and organizes them into a knowledge graph so that entities like a specific building, owner, or loan are linked and consistent. On top of that foundation sits Agent.STUDIO, a product that lets a firm design, deploy, and scale purpose-built AI agents and natural-language queries that run against its own connected data.

Cherre was founded in 2016 and is headquartered in New York City. The company has raised roughly $105 million from venture investors and positions itself as a data infrastructure layer rather than a single-purpose application. Agent.STUDIO, launched in 2025, is model-agnostic, meaning clients can choose and switch between different underlying AI models rather than being locked into one, and it is delivered with hands-on support alongside the software.

Core Capabilities

Data integration and cleansing

Cherre connects internal systems and external feeds, then standardizes and de-duplicates the records so that the same property or entity is represented consistently. This resolution work is the unglamorous but essential step that makes everything downstream reliable.

Knowledge graph and data models

The platform maps connected data into a knowledge graph built on universal and semantic data models, linking buildings, owners, loans, and markets. Relationships between entities become queryable, which is what lets an agent trace, for example, every asset tied to a particular sponsor.

Agent.STUDIO agentic workflows

Agent.STUDIO lets a firm build and deploy AI agents and natural-language queries that operate over its connected data. Because it is model-agnostic, teams can pick the model that fits a task and swap it later as better options appear.

Governance and security

Client data environments are governed by customizable access controls and are not used to train external models, which matters for firms handling sensitive financial and tenant information.

Strengths

• Data-first architecture: Cherre invests in connecting and resolving data before layering on AI, which is the right order and a genuine differentiator versus thin query wrappers.
• Knowledge graph depth: Linking entities into a graph enables relationship queries that flat databases and spreadsheets cannot easily answer.
• Model-agnostic design: Firms are not locked to one AI provider and can adopt newer models as the field moves quickly.
• Enterprise governance: Access controls and a no-training data posture fit the compliance needs of institutional real estate firms.

Limitations & Considerations

• The AI is only as good as the connected data. If a firm's source systems are incomplete, stale, or poorly maintained, the agents built on top will inherit those gaps — Cherre reduces but does not eliminate the need for data discipline.
• This is infrastructure, not a turnkey app. Realizing value requires integration work, data mapping, and often the hands-on service engagement; it is not a self-serve tool a solo agent installs in an afternoon.
• Enterprise cost and commitment. The platform is aimed at institutional clients, so pricing and onboarding assume a serious data operation rather than a small team.
• Outputs still need human judgment. Agent responses and natural-language query results should be validated by analysts, especially for decisions touching valuation, lending, or anything subject to Fair Housing and fair-lending rules.

Best Use Cases

Getting Started

1. Inventory the internal systems and external data feeds your firm relies on and where they currently live.
2. Engage Cherre to connect and resolve those sources into the universal data model and knowledge graph.
3. Use Agent.STUDIO to build a first narrow agent or natural-language query for a well-defined workflow.
4. Validate the agent's outputs against known-good data before expanding its scope or automating decisions.

Key Takeaways

• Cherre is a real estate data-integration and knowledge-graph platform founded in 2016, based in New York City, and backed by roughly $105 million in funding.
• Its Agent.STUDIO product lets firms build and deploy model-agnostic AI agents and natural-language queries over their connected data.
• The core value is data plumbing first — unifying fragmented property data into one consistent, linked foundation.
• The AI agent layer is only as reliable as the connected data beneath it, and outputs still require human validation.