openOM

openOM vs Cherre: which platform standardizes CRE data better?

By , Independent Software Engineer · Creator of openOM · 8 min read · Updated Oct 12, 2026

Short answer

openOM standardizes CRE data better at the source: brokers embed a schema-validated, broker-asserted payload inside each offering memorandum PDF and mirror it as JSON-LD, with no inference in the core and an open-source license. Cherre standardizes better across a whole portfolio: it connects, cleans and resolves public, third-party and proprietary datasets into one entity model served through a GraphQL API. Choose openOM for inbound deal documents and Cherre for enterprise-wide data.

Architectural Paradigms: openOM and Cherre Data Standardization Models#

openOM vs Cherre comes down to where each one standardizes CRE data. openOM puts a validated data schema inside each offering memorandum PDF at the moment a broker publishes it (github.com). Cherre connects, cleans and resolves public, third-party and proprietary datasets after the fact, in one central platform (apis.io).

The fundamental division in commercial real estate (CRE) data standardization is not schema flexibility, but evidentiary status: Cherre standardizes data through probabilistic post-hoc resolution of external facts, whereas openOM treats real estate data as signed, auditable claims where provenance, not objective consensus, is the primary unit of standard.

The following architectural comparison illustrates these fundamentally different strategies for data management:

Attribute openOM Cherre
Primary Delivery Model Open standard and client-side toolchain (github.com) Centralized enterprise data integration platform (apis.io)
Standardization Location Document level inside the PDF offering memorandum (github.com) Centralized cloud data platform and data fabric (apis.io), (sfailabs.com)
Data Encoding Format Embedded document payload mirrored as JSON-LD (github.com) Universal data model and connected graph modules (apis.io), (bintelligence.com)
Operating Scale Single-document ingestion and validation (github.com) Resolves entities across real estate asset portfolios (propmodo.com)

Per openOM project documentation, openOM is designed as an open standard and toolchain that embeds machine-readable, broker-asserted data payloads within commercial real estate offering memorandum PDFs and mirrors them as JSON-LD (github.com). This approach enforces data modeling at document authoring, turning an offering memorandum (OM) into an extractable asset at the source.

In contrast, Cherre functions as a real-estate data-integration and property-intelligence platform that connects, cleans, and resolves public, third-party, and proprietary real-estate datasets into a single environment (apis.io). As detailed in public materials cited by SFAI Labs, Cherre resolves more than four billion real estate entities representing about $4 trillion (sfailabs.com) in assets, supported by a data fabric covering 3.3 billion-plus addresses. The architectural trade-off balances an edge-distributed document payload against an enterprise data aggregation engine (github.com),.

Data Provenance: Broker Assertions Versus Unified Property Records#

Data provenance in computer science literature is defined as information that helps determine the derivation history of a data product starting from original sources (scholarworks.iu.edu). In commercial acquisitions, conflating marketing claims with historical property truths degrades algorithmic underwriting.

CRE Data Lineage
  1. Source Generation
  2. Author Attribution
  3. Payload Verification
  4. Downstream Consumption

To maintain clear lineage, openOM payloads require assertedBy and assertedDate fields to record who asserted the opinion and as of when, treating offering memorandum data as broker assertions rather than verified market facts (github.com). Because an offering memorandum functions as a promotional document representing a seller-approved valuation opinion, openOM deliberately tracks the derivation identity rather than guaranteeing market reality.

Enterprise integration platforms approach provenance through master records. Cherre consolidates disparate data sets, aiming to build a centralized single source of truth across conflicting municipal records, partner reports, and operational ledgers (apis.io). This structural distinction reflects different operational goals: openOM documents legal assertions without altering broker attribution, while centralized integration models resolve conflicting sources into a unified entity record (github.com),.

Schema Validation in openOM vs Entity Resolution in Cherre#

openOM validates each offering memorandum payload against a fixed data schema with deterministic rules, while Cherre resolves identities across many sources into one record. The first checks that a single document is well formed and internally consistent. The second reconciles records that disagree. Both standardize CRE data, but at different points in the deal lifecycle.

An offering memorandum schema requires deterministic boundary validation. As documented in the openOM repository, openOM enforces a deterministic core where the engine, Model Context Protocol server, and consumer tooling make zero large language model or inference calls, restricting inference-assisted mapping strictly to the authoring layer (github.com). In openOM's validation design, schema errors block processing, whereas internal consistency warnings (such as Net Operating Income divided by purchase price versus capitalization rate) never block, verifying provenance and internal consistency rather than market truth.

For a simple acquisition model, internal consistency checks evaluate capitalization rate formulas directly:

Capitalization Rate = Net Operating Income / Purchase Price

If an offering memorandum lists a Net Operating Income (NOI) alongside an acquisition price, the consistency engine calculates:

$600,000 / $10,000,000 = 0.06

A mismatched stated cap rate triggers a non-blocking consistency warning, but the document passes parsing as long as its schema types are valid (github.com).

Instead of checking one document against a schema, Cherre resolves conflicting records across public, third-party and proprietary sources into one entity model (apis.io). The divergence is structural: openOM enforces strict, rule-based schema boundaries on author payloads without inference calls (github.com), while Cherre resolves entities across incoming public, third-party and proprietary datasets, (propmodo.com).

Cherre Knowledge Graph and Data Governance#

Managing multi-source real estate data at enterprise scale requires rigorous ingestion governance and shared ontologies. Cherre coordinates inbound partner data through structured pipelines and quality control monitoring.

Cherre describes itself as an open data platform that can integrate with a range of property management systems and third-party data sources (citybiz.co). In practice this is master data management for real estate: many feeds go in, and one governed record per property, owner or legal entity comes out (apis.io).

To maintain ingestion integrity, Cherre's data architecture utilizes a quality control object that enables clients to monitor pipeline data observability, business logic rules, and SOC 1 and SOC 2 compliance audibility (bintelligence.com). This governance layer verifies pipeline data quality before data points enter downstream models.

Once validated, Cherre connects ingested data to a core module featuring a universal data model, a semantic data model, and a knowledge graph encompassing over four billion legal entities for entity resolution (bintelligence.com). This connected graph architecture structures heterogeneous market feeds into an interconnected map of real asset properties, owners, and legal entities.

API Access: Cherre GraphQL API vs openOM JSON-LD and MCP#

Cherre delivers standardized data through one central GraphQL API and pushes data into cloud warehouses, while openOM ships the data inside the document itself and offers a free read-only endpoint. Which is easier depends on whether your team wants a managed feed or a file it can parse locally.

As recorded in API profiling by APIs.io, Cherre serves resolved real estate datasets back through a single GraphQL API built on Hasura (apis.io). API Evangelist documentation shows that Cherre exposes connected third-party marketplace data and proprietary portfolio data joined to a resolved knowledge graph across a unified GraphQL API surface at https://api.cherre.com/graphql (github.com).

For downstream consumption across institutional infrastructure, Cherre enables data downstream transmission to source systems for interoperability, cloud environments like Snowflake or Azure, and AI platforms via its Alpha platform (bintelligence.com). This approach allows enterprise engineering teams to query integrated property records via an API endpoint or push synced datasets directly into institutional cloud warehouses (github.com),.

openOM takes a lighter approach to its application programming interface. The payload travels inside the PDF and is mirrored as JSON-LD on the web, so any ETL pipeline can read it straight from the file (github.com). For agents and portals, openOM also runs a free public MCP endpoint at mcp.openom.app with two read-only tools, om_read and om_validate, and needs no API key and charges no per-call cost. Teams that want the full tool set can self-host the MCP server.

Cost and Licensing: Open-Source openOM vs Commercial Cherre#

openOM costs nothing to license because its toolchain and specification are open source, but your team runs the tooling. Cherre is a managed commercial service, so it carries a vendor contract but also takes integration work off your plate. The right choice depends on budget, in-house engineering time and how many data sources you need to join.

The cost question is really about what you license and what you run yourself. Cherre is a commercial platform, so plan for a vendor contract. openOM is open source: the toolchain is MIT licensed and the specification is CC-BY-4.0 (github.com). Three differences matter most:

  1. Direct Licensing Fees: openOM provides an open standard with zero recurring platform subscription or per-call query fees (github.com).
  2. Infrastructure Footprint: openOM uses client-side or on-device parsing tools with no central hosting requirements, whereas centralized platforms rely on cloud data warehouse infrastructure, (bintelligence.com).
  3. Connector Maintenance: Cherre maintains the integrations to third-party sources and property management systems for you (citybiz.co), while teams using an open payload read standardized files directly.

Organizations weighing total cost of ownership must evaluate whether they require a fully managed data ingestion pipeline or can satisfy their pipeline requirements with a lightweight, standard document structure (github.com).

Which Platform Standardizes CRE Data Better for Your Team?#

openOM is the better fit when your bottleneck is reading inbound broker documents, and Cherre is the better fit when you must unify many internal and external datasets across a portfolio. Most teams can decide by looking at where their analysts lose the most time today.

Cherre operates as an open data platform capable of integrating with third-party data sources and diverse property management systems (citybiz.co). This capability suits institutional asset managers who must reconcile internal accounting databases, historical performance metrics, and external vendor data feeds across large existing portfolios, (pulse2.com).

Conversely, openOM fits acquisition workflows that ingest inbound marketing materials from external brokerages (github.com). Because openOM enforces data structures at the document layer, it enables investment teams to read acquisition metrics directly from offering memoranda without post-processing guesswork.

To determine which model fits your technical needs, evaluate your requirements against this checklist:

  1. Identify whether your immediate bottleneck is standardizing inbound broker acquisition PDFs or unifying enterprise portfolio databases.
  2. Verify if your downstream analytics rely on strict broker provenance or require resolved property identities across municipal data feeds.
  3. Review your budget and team: open-source tooling you run yourself, or a managed enterprise platform priced by contract.

For investment teams focused on acquisition efficiency, standardizing marketing documents is the natural initial step: examine the open specification to make incoming offering memoranda machine-readable across your screening pipelines.

Feature and implementation matrix comparing openOM and Cherre
CapabilityopenOMCherre
Licensing modelDual-licensed (MIT toolchain, CC-BY-4.0 specification)Proprietary commercial enterprise contracts
Pricing / Cost structureOpen source; free public read endpoint with no per-call costCommercial platform; pricing by contract
Inference and AI executionClient-side only at authoring layer; zero inference in engine/MCPCentral platform that cleans and resolves entities across datasets
Primary query interfaceEmbedded payload mirrored as JSON-LD, plus a free read-only MCP endpointSingle GraphQL API built on Hasura
Downstream export targetsDirect local extraction from documentsSource systems, cloud environments (Snowflake, Azure), AI platforms
Governance & compliance controlsDeterministic schema validation and consistency warningsQuality control object covering observability, business rules, SOC 1/2
Data validation and provenance decision matrix
DimensionopenOM Rule-Based ModelCherre Resolution Model
Core objectiveVerify broker attribution, unaltered state, and schema complianceResolve fragmented identities across public and proprietary sources
Data definitionBroker-asserted valuation opinion as of a specific dateStandardized, governed master real estate entities
Required attribution fieldsassertedBy and assertedDate fieldsMapped partner/internal provider identifiers
Error handling mechanismSchema errors block; consistency warnings never blockConflicting records resolved into one entity model
Source dependencySelf-contained inside offering memorandum PDFsPublic, third-party and proprietary datasets

CRE Data Pipeline & Standardization TCO Calculator

Compares the Total Cost of Ownership (TCO) and labor savings between manual ingestion, adopting an enterprise data fabric platform (Cherre), and using an open-source schema standard toolchain (openOM) for CRE deal analysis.

Current Baseline Manual Labor Cost: $72,000

Enterprise Platform Cost: $100,000

openOM Workflow Labor Cost (assuming 20% manual fallback + review): $20,400

Net Annual Savings with openOM Standard: $51,600

Net Annual Labor Savings vs Cherre Cost: -$28,000

Related reading

Sources