AI Infrastructure & MLOps for Real Estate

Real estate AI lives or dies on documents: leases, rent rolls, appraisals, loan tapes, and title that change shape from one deal to the next. A model that reads them well in a demo drifts the moment a new lender template or a fresh asset class arrives, and a silent extraction error in underwriting can mispricing a deal or trip an AML check. MLOps is what keeps that from happening. We build the evaluation, monitoring, and CI/CD that catch document drift before it reaches a credit committee, so the AI you ship into leasing, underwriting, and project delivery stays accurate as your portfolio and your partners' paperwork evolve.

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How we deliver it

AI Infrastructure & MLOps, built for real estate

We build evaluation sets from your real lease and loan documents, with golden answers for the fields that drive underwriting and AML decisions, so every model change is scored before it ships.

We monitor extraction accuracy, latency, and cost per document in production, with alerts when a new lender template or asset type starts degrading quality.

We wire model and prompt changes through a CI/CD eval gate, so an update tuned for office leases cannot quietly regress on retail or multifamily.

Everything runs in your cloud against your deal data, with audit trails that satisfy lender and partner controls.

Where it pays off in real estate

Underwriting eval gate

Score every model change against a labeled set of real loan documents so accuracy never regresses silently before a credit committee sees the output.

Document drift alerts

Detect when a new lender template or lease format pushes extraction quality down, and alert your team before bad terms flow into a deal.

AML check monitoring

Track the AI screening transactions and counterparties for false-negative drift, so compliance keeps a defensible record of model behavior over time.

Cost per deal

Tune routing and caching across document-heavy workflows so processing thousands of pages per deal stays fast and affordable at portfolio scale.

Real estate clients typically cut cost per document by a third while catching extraction regressions before they reach underwriting, turning a fragile prototype into a system the credit team trusts every quarter.

Real Estate AI, answered

We build evaluation sets segmented by document type, lender, and asset class, each with golden answers for the fields that matter. When a new template appears, it becomes its own eval slice, so you know exactly where quality holds and where it slips.

Yes. Every model version, eval result, and production decision is logged in your environment with timestamps and inputs. That gives you a defensible record of how the AI behaved on any given deal, which is what partner and AML controls require.

That is the point of the eval gate and production monitoring together. Changes are scored before release, and live accuracy is tracked per document type, so a regression on, say, rent rolls triggers an alert rather than a mispriced deal.