The Problem Nobody Admits Out Loud
Cherre solves one of the most expensive problems in institutional real estate data silos. Property records from fifty different databases, all describing the same asset in slightly different ways, finally unified into a single queryable fabric.
The technology works. The problem is what happens after it's deployed
"We spent $200k on Cherre and we still have an analyst manually building the investment memo. The pipe is perfect. The work it was supposed to eliminate is still there."
Cherre connects the data. It doesn't interpret it, act on it, or translate it into the construction and financial workflows that need to receive it.
What It Actually Feels Like
"It's an API-first platform. Without an in-house data engineering team, it's a very expensive box of parts.""Cherre unified our data perfectly. It didn't fix the fact that our property managers weren't updating their systems. We got a perfectly unified lie.""We pay for Cherre plus CoStar plus REIS plus three other subscriptions. The cost of admission is significant.""Implementation took nine months. We were promised six.""It gives us the data. It doesn't write the investment memo. We still need an analyst for that."
The Tech Reality
1. High technical debt for non-engineering organisations Cherre is an API-first platform built for organisations with data engineering capability. For real estate firms without a dedicated data team, it arrives as a sophisticated infrastructure without the operational layer to use it.
2. Garbage in, garbage out Cherre's entity resolution is powerful it can recognise that "123 Main St" and "123 Main Street, Suite A" are the same asset across fifty databases. But it can't fix the underlying data quality problem. If property managers aren't updating their systems, Cherre delivers a perfectly unified, perfectly inaccurate picture.
3. Cost of admission Cherre fees sit on top of the underlying data subscriptions it connects CoStar, REIS, and others. The total cost of a fully deployed Cherre environment is significantly higher than the platform cost alone.
4. Implementation lag Mapping an enterprise's custom data schema to the Cherre model takes six to twelve months in practice.
5. No work product Cherre provides the data. It doesn't produce the investment memo, the underwriting model, or the risk analysis. An analyst still sits between the Cherre output and the decision.
The Construction Reality
For construction teams, the Cherre problem isn't between property databases it's between the preconstruction office and the job site.
The static estimate An estimator builds a budget in January. Construction starts in June. Material prices have shifted ten percent. The estimate hasn't updated. The PM inherits a budget built on assumptions that are now six months old.
Drawing version chaos Drawing revisions happen continuously through design development. When the estimator is pricing from Rev 2 while the PM is already coordinating with subcontractors on Rev 4, the scope gap between them becomes a change order waiting to happen.
Manual subcontractor levelling Three HVAC bids arrive in different formats with different line item structures. An estimator spends hours manually normalising them in Excel to compare apples to apples.
Double-entry between estimating and accounting Buyout data that lives in the estimating environment gets manually typed into Sage or Vista by the PM. Every manual transcription is a data quality risk.
Reactive financial visibility The PM finds out the project is over budget during the month-end close after the subcontractor invoice has already been approved.
The Economic Layer
Investment underwriting depends on accurate, current data about comparable assets, market conditions, and portfolio performance. When Cherre's data fabric is clean but the systems feeding it aren't, the underwriting model reflects historical discipline failures rather than current market reality.
For construction-heavy portfolios, the gap between estimated project cost and actual project cost is the most significant variable affecting fund-level returns. When construction cost actuals don't flow automatically into the Cherre environment, the portfolio picture that investors and executives are making decisions against is missing the most volatile line item in the asset's financial model.
Building the Right System Around Cherre
Automated data quality enforcement Instead of depending on property managers and project teams to update source systems manually, we build trigger-based data capture workflows that update Cherre-connected systems from operational events.
Construction-to-portfolio data pipeline Procore project actuals, Sage job cost data, and field reporting sync automatically into the Cherre environment. Construction cost variance flows into portfolio-level reporting in real time.
BIM-to-cost automation When a designer changes a wall type or structural system in the 3D model, an automation layer triggers a cost impact calculation and alerts the estimator and PM automatically.
Automated work product generation Instead of an analyst manually interpreting Cherre data, structured output templates pull directly from the Cherre API generating Go/No-Go analyses, bid levelling summaries, and portfolio performance reports from live data.
Entity resolution for construction data The same entity resolution logic that makes Cherre powerful for property records applies to construction data matching subcontractor names, cost codes, and drawing references across Procore, Sage, Bluebeam, and estimating software.
Before vs. After
Before
- Cherre data fabric is clean but downstream consumption requires manual analyst work
- Source system data quality depends on human update discipline
- Construction cost actuals don't flow into portfolio reporting automatically
- Drawing revisions don't trigger cost impact alerts
- Investment memos and underwriting models are manually assembled from Cherre outputs
After
- Consumption workflows pull directly from Cherre API — work products generate automatically
- Operational triggers update source systems without manual data entry
- Construction actuals sync into Cherre and portfolio reporting in real time
- Drawing changes trigger automated cost impact calculations and PM alerts
- Portfolio intelligence reflects current construction and financial reality continuously
Unified Portfolio Intelligence (Join.build Integration)
For real estate investors and developers managing active construction programmes alongside a live portfolio, Monexo implements a full unified intelligence layer connecting Cherre, Procore, Sage, and Join. Join acts as the Decision Intelligence layer for the construction-to-portfolio handoff creating a decision log that links every budget change to a specific stakeholder and drawing version, and automating the value engineering process.
The Real Insight
Cherre's value proposition is genuine. The ability to run a query across ten thousand properties in seconds is a real capability that transforms portfolio analysis at scale.
The problem is that the data fabric Cherre creates is only as useful as the systems built to consume it. Cherre built the data infrastructure. The operational layer around it was never built.
We build the system.