Make your firm AI-native.

Three phases (Build, Operate, Scale) plus Strategy & Advisory as a standalone option.

We deploy your data infrastructure once into your Microsoft Azure tenant. Every engagement kicks off with a paid discovery phase that produces an architecture blueprint, AI governance framework, and per-department SOWs.

Once live, AI workflows run every day on your platform. We keep it on the current generation of every component as the frontier moves.

Forward-deployed engineers ship new workflows, agents, and integrations every quarter as your firm becomes AI-native across every department.

Pricing: a one-time deployment fee scoped during the discovery phase, plus a recurring monthly Platform License and optional committed engineering hours for new workflows and ongoing improvements. Strategy & Advisory is available standalone for firms that want only the blueprint and roadmap.

An ontology is a shared model of the things your firm thinks about (properties, deals, units, leases, tenants, comps, funds, LPs) and how they relate. Without one, every system in your stack disagrees about what a property is, what counts in NOI, and how occupancy rolls up to the fund.

We deploy a CRE-native ontology as part of the data layer: dimension tables for property, deal, fund, and counterparty; fact tables for financials, rent rolls, and comps; reference tables for chart-of-accounts mappings; and graph relationships connecting it all. The same number ties across Yardi, Excel, Power BI, and your AI agents.

The ontology is also why agents can answer multi-step questions reliably. They query a model that already understands what these objects are, not a pile of disconnected tables.

Yardi, MRI, AppFolio, Entrata, CoStar, ESRI, Monday.com, Notion, Power BI, SharePoint, Microsoft Fabric, Excel, Slack, Teams, and most CRE-specific tools.

If it has an API or data export, we can connect it. We deploy on Microsoft Azure inside your tenant, configured for CRE workflows and integrated with the systems your team already runs.

Our founder leads every engagement directly, supported by senior engineers who specialize in CRE data systems and cloud architecture.

Engineers are forward-deployed: they embed with your team to deploy the platform, then operate and evolve it alongside you under committed engineering hours.

Customer success is led by real estate operators who speak your language: deals, pro formas, waterfalls, lease economics, cap rates, schedules of REO.

Every engagement starts with an NDA. Your data stays in your Microsoft Azure tenant, not ours.

Everything we deploy runs inside your Microsoft Azure tenant. Your data stays in your tenant and is not routed through RealQuant infrastructure.

You receive: your data, your custom-deliverable configurations (chart-of-accounts mappings, benchmarking views, client-specific schema extensions), your dashboards, your reports, and full documentation.

RealQuant retains ownership of the underlying platform: the data model, parsers, agent framework, connector library, and reusable templates. Your engagement includes a perpetual license to run the deployed instance for your firm’s internal business purposes. Updates, new versions, security patches, and new features flow through the Platform License plus optional committed engineering hours.

Full terms are in the Services Agreement and applicable Statements of Work.

Yes. Strategy & Advisory is a standalone product covering AI readiness assessments, governance frameworks, vendor evaluation, technology roadmaps, and executive AI training.

You walk away with a plan, assigned owners, and build-or-buy recommendations. If you decide to build, we implement and operate it.

When AI agents touch underwriting assumptions, investor reports, or deal recommendations, your investment committee needs to trust the output. AI governance is the framework that makes that possible. It is enforced by the AI harness layer described below.

We build source attribution so every AI-generated number traces back to the specific page and table in the source document. We implement hallucination detection to flag outputs with no supporting evidence. And we design compliance policies that ensure AI-assisted work is reviewed before it reaches decision-makers.

If you are deploying AI agents that touch investment decisions, governance is not optional. It is how you defend your process to LPs, auditors, and regulators.

AI never does the math. Every number in every output comes from deterministic models that sit between your data layer and the AI interface. Regressions, forecasts, Monte Carlo, variance attribution, scoring, all of it runs in code you can audit.

The AI's job is to translate your question into the right model call and present the answer with citations. If AI were generating the numbers, it would hallucinate them. The whole architecture is designed so it never has to.

This is the difference between an AI that drafts an IC memo and an AI that invents an IRR. We ship the first. We refuse to ship the second.

A harness is the layer that constrains an LLM to safe, traceable behavior. It defines which tools the model can call, which data it can see, how its outputs get cited, and what gets logged for audit.

We build a CRE-specific harness so AI agents can ask your deterministic models for answers, return cited responses your IC can defend, and never improvise on numbers. The harness is also what lets us swap in a new frontier model with a config change instead of a rebuild.

Without a harness, an LLM in front of your data is a liability. With one, it becomes the natural-language interface to a system your team can actually defend in IC.

Quant is a capability that spans every engagement phase. Once your clean data foundation is built, we layer the models institutional quant funds run on capital markets, adapted for CRE: regression, forecasting, probabilistic scenarios, predictive scoring, Monte Carlo, variance attribution.

Concretely: pipeline scoring ranks every deal against your investment view. Seller-propensity models surface owners likely to sell. Submarket scoring evaluates every market on supply, demographics, and demand signals. Hold-period optimization tells you when to exit. Variance attribution explains why actuals diverged from underwriting.

Every signal maps back to a line item your IC already reviews: pro formas, waterfalls, lease economics. No black boxes. No model in search of a deal.