AI / ML Solutions for Real Estateproduction AI
We build AI that ships to production — not demos. From LLM automation that stripped $6.5M out of a real estate provider's operating costs to ML models that cut payment errors by 95%, our work is measured in business outcomes, with MLOps that keeps models accurate long after launch.
What we do
Full-spectrum capability, built for PropTech platforms and proven on real engagements.
LLM Automation & Document Intelligence
Extract, classify, and route leases, due-diligence reports, and filings. We map high-volume, document-heavy workflows and automate the ones an LLM handles with equal or greater accuracy.
Predictive Analytics & Forecasting
Time-series degradation models and portfolio forecasting that turn raw telemetry and transaction data into decisions made before failures or churn occur.
Anomaly Detection
Models that separate normal operational variance from early-stage failure signals with high precision — the core of our outage-prediction work.
ML Model Development
Custom models trained on real estate data, with measurable accuracy thresholds defined before a line of pipeline code is written.
Cascade & Decision Systems
ML routing that picks the best path in real time — as in our payment-gateway cascade that reduced fluctuations by 40% across diverse providers.
MLOps
Automated evaluation triggered by metric drift, continuous retraining pipelines, and counterfactual logging so models stay accurate as the data distribution shifts.
How our models stay accurate in production
AI in real estate is not a data-science problem alone. The pipeline, evaluation loop, and human review are what turn model output into usable business value.
$6.5M out of operating costs, POC to $1M MRR
After a large acquisition, a leading European real estate provider committed to cut total operating costs by $6.5M. The instinct was headcount reduction. The real answer was to automate every asset-manager workflow an LLM could handle — lease review, covenant monitoring, rent reconciliation, reporting.
VSBD was the implementation partner. The first POC moved to production in December 2023 — five months after engagement — by focusing on a single, high-value, measurable workflow rather than boiling the ocean.
Presented at the PropTech Summit in Germany, the solution was awarded the #1 Asset & Portfolio Management Tool in 2024, and the platform reached $1M MRR by January 2025.
Best practices we apply
The principles behind the outcomes — learned by delivering, not theorizing.
Define accuracy thresholds before you build
Set measurable targets (e.g. ≥85% extraction accuracy) up front so success is objective, not a matter of opinion at demo time.
Keep a human in the loop
The best automation pairs model output with a feasible review step as the quality gate — that is what makes outputs trustworthy in a real business.
Treat drift as inevitable
Acting on a prediction changes the data distribution. Automated evaluation and continuous retraining are non-negotiable, not phase-two nice-to-haves.
Engineer the foundation, not just the model
Data pipelines, integration architecture, review UX, and monitoring decide whether a model is usable. AI projects fail when treated as pure AI projects.
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