Ask your business a question. Get the answer from your own data.
ORIL unifies your PMS, ERP, project, financial, and document systems into one structured data model, then adds AI that answers your teams’ questions, citing the source records behind every answer.
Real estate and construction teams run on separate operational, financial, project, and document systems. Each holds part of the picture, but none shows the whole business.
The result:
More data doesn’t help if no one can find it or trust it.
Data enablement means making your business data usable. Instead of adding another tool, ORIL connects the systems you already use, such as PMS, ERP, project tools, and document stores, and structures their data into one model. AI then works on that connected business data, not isolated files, so your teams can search, analyze, and act on it in plain language.
Who this is for:
Identify where your data is fragmented, what can be connected, and which use case could deliver value first.
Leases, tenants, units, rent roll, maintenance
Contacts, deals, investor relationships
Ownership, mortgages, valuations, rental comps, transactions, and market indices
Budgets, forecasts, general ledger, consolidation
Models, drawings, schedules, RFIs, cost data
Contracts, permits, reports, O&M manuals
Progress, tasks, change orders, project data
Spreadsheets, presentations, correspondence
Sensors, BMS, access control, energy data
Asset registers, COBie handover data, O&M records
Permits, zoning, certifications, carbon and energy data
HR, legal, procurement and other business data
Using a system not listed? If it has an API, an export or a database, we can assess how to connect it, subject to access, licensing and data availability. Data that isn’t in any system yet, such as paper records or off-site archives, is flagged during Discovery so you know exactly what’s missing.
The same data can answer different questions across your organization. Role-based access helps each team find the information relevant to its work.
Start with the use case that matters most to your business and identify what it would take to make it work.
years building custom software and data solutions
products delivered across Real Estate, PropTech, ConstructionTech, and GreenTech
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It’s the work of turning scattered business data into something teams can use. For a real estate or construction company, that means bringing PMS, ERP, project, and document data into one model, so teams can search, report, and use AI on it, with every answer traced to its source.
It gives your teams one place to find information, track performance, and automate routine work, using your own connected data. Depending on scope, it can include:
Scope depends on your data, priorities, and business requirements.
Most systems real estate and construction companies use, including PMS, ERP, CRM, construction, document, IoT, and data provider platforms. If a system has an API, export, or database, we can assess how to connect it, subject to access and licensing.
That’s where the process starts. During Discovery, we map what data exists, where it lives, how reliable it is, and what’s missing. Every source is marked as known, assumed or unknown, so you see the real picture, including records held off-site or by third parties. The data is then cleaned, mapped, and structured according to the priorities and scope of the project.
Answer quality depends on the quality, coverage, and freshness of connected data, as well as the AI architecture and use case. Source references and validation workflows can help users check answers. They do not guarantee the underlying data is correct, and the system can only answer from what is connected. Human review can be included for sensitive or high-impact processes.
Not much. ORIL handles discovery, integration, data structuring, AI implementation, and rollout, so you don’t need internal engineers. From your side, we need:
Involvement is highest during Discovery and testing. Exact roles and time depend on your systems and project scope.
Yes. PropTech companies can use it as an internal knowledge layer or as a data and AI foundation for new product features, customer-facing assistants, and data-driven services. Product use cases add requirements such as external-user permissions, reliability, scalability and data licensing, which are scoped separately.
Your data stays under your control. The setup is agreed during Discovery and can include:
Specific controls, data retention, and logging are agreed per project.
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