The enterprise data foundation · Starting in real estate
The enterprise data foundation for real estate, built and kept current automatically
Reporting, analytics and AI are only as good as the data they are given, and raw application data is not good enough: it has to be modelled first. Sereen builds the semantic model of each application your business runs on, links them into one model of the company, and keeps it current as the systems and the people change. Built by agents that learn how the software actually behaves. Starting with the systems real estate runs on.
Why it matters
The model of what the data means is rebuilt by hand, everywhere.
Written down almost nowhere
Every operator runs on a handful of enterprise applications: property management, accounting, leasing, and the spreadsheets that connect them. What the data means, which fields hold it and how one application maps to the next lives in report definitions, consultants' project files and a few people's heads.
Rebuilt for every request and every tool
Every report outside the standard pack, every reconciliation across two systems and every new tool that needs clean data starts by rebuilding that knowledge by hand. Consultants rebuild it per project, internal teams per tool, third-party managers per client.
Now the constraint on AI
The model is the foundation every reporting, analytics and AI initiative depends on. Today it is built over months, at a cost that has to pay back within a year, and starts decaying the day the systems or the people change. In many companies it does not exist at all.
What we build
The model is the product. Every tool you run consumes it.
A semantic model of each application
Sereen learns how each application actually behaves, its entities, fields, workflows, calculations and reporting conventions, and encodes it as a semantic model of your specific configuration, not the vendor's manual.
One model of the company
The application models are linked into one model of the company across its systems: how a lease in one application becomes a receivable in the next, and where the reconciliations sit. Built once, shared by your warehouse, BI, AI tools and migrations.
Kept current, and verifiable
Maintained as systems, configuration and people change. Every figure produced from the model is traceable to the screen or query it came from, and every recurring run is replayable.
Where Sereen sits
One layer, shared by every tool that stores, shows, answers from or moves your data.
Today that layer is rebuilt by hand for each tool, by consultants or internal teams, and decays. With Sereen it is built once, shared, and maintained.
Sereen is not a business intelligence tool, a data warehouse or a migration project, and it does not replace any of them. It is the layer they all depend on, built and maintained automatically, whether it exists today or not.
How it works
Explore. Model. Maintain.
Sereen doesn't read about your systems. An agent learns them the way a new analyst would, except systematically and continuously, and keeps learning as they change.
Explore
The agent operates the application through its interface, reads the underlying data and whatever documentation exists, and observes how the configured system actually responds.
Model
What it finds is triangulated across independent sources into a semantic model of how your configuration really behaves. Where sources disagree, and they usually do, it tests the application to find out what it actually does.
Maintain
The model has four layers: what the agent observed, what it concluded, what your team confirmed, and what is reused the next time the same work comes round. It is kept current as the system changes, and every figure is traceable after it runs.
See it in action
An agent learning a property management system, live.
Ninety seconds. The agent works through the application on the left; on the right, its semantic model of what the software is and how it behaves builds up as it goes.
Recorded on our purpose-built property-management test application. No customer data.
Results
We publish what our agents actually do.
Numbers from our own evaluation harness, on a purpose-built property-management test application. Every answer is auditable back to the exact screens the agent saw and the database state that confirmed it.
on our ten-question reporting suite (occupancy, delinquency, lease expirations, NOI, expenses), achieved on two different models, every figure cross-checked before it is returned.
for a full ten-question reporting run. $0.22 on a lower-cost model, $2.05 on a frontier model, identical 10/10 result. As models get cheaper, the economics only improve.
to build the semantic model shown below from zero knowledge: 6 business entities, 43 annotated fields, and 9 relationships in one autonomous session: no documentation, no source code, no human explanation.
The semantic model, exactly as the agent built it, with no documentation, no source code, and no human explanation. Solid = declared constraint harvested from the application; dashed = behavior observed by acting in the UI; every link carries a confidence score.
Where we start
One application, and the reports that come out of it
The first engagement is one application and its recurring reports: the highest-volume, highest-friction requests in real estate operations, and the ones that queue longest behind scarce specialists. The model is built inside your own environment, on the systems you already run, vendor-built or in-house, and nothing leaves it.
Get in touch
If your data lives in systems only a few people truly understand, we'd like to talk.
We're speaking with real estate operators about how reporting and data work in their organizations today: what's slow, what's manual, and what should just be an answer.
Email us adam@sereen.ai