Predict Commercial Real Estate Deal Closures with Custom AI

CRE brokerages often rely on the pipeline stages in CRMs like Apto, Buildout, or even Salesforce. These tools track deals but cannot predict them. A deal is marked "LOI Sent" or "Due Diligence" based on manual broker input, offering a static snapshot, not a probabilistic forecast. The system has no opinion on whether a $5M deal at 50% is more or less likely to close than a $2M deal at 75%.

Consider a 15-person investment firm managing 50 active deals. Two senior partners review the pipeline weekly. They spend hours debating which deals need their attention, relying on broker intuition and gut feelings. A promising industrial portfolio goes cold because the broker leading it is focused on a larger, flashier office deal that ultimately fails during financing. The CRM showed both deals at the "Touring" stage, giving no signal of their true closure probability.

These off-the-shelf CRMs are databases with a user interface, not analytical engines. Their architecture is designed for data entry and retrieval, not for learning from historical patterns. They cannot parse unstructured data like broker notes or email sentiment to find predictive signals. They lack the ability to connect to external data sources, like property-level debt information or local market absorption rates, that are critical for accurate CRE forecasting.

The result is misallocated resources. Partners waste time on deals destined to fail. Marketing budgets are spent on campaigns that attract low-probability leads. Junior brokers don't learn what a "good" deal actually looks like from a data perspective, slowing their development. The firm’s capital deployment and revenue forecasts remain unreliable.

The engagement would begin with a data audit. Syntora connects to your CRM (Apto, Buildout, Salesforce) and extracts 12-24 months of deal history. This audit identifies predictive features and flags data quality gaps, like missing close dates or inconsistent stage names. You would receive a clear report on data readiness and a list of the top 50 potential features for the model.

The technical approach would use a Python-based pipeline to process the data and train a gradient boosted model. This model would be wrapped in a FastAPI service deployed on AWS Lambda for low-cost, serverless execution. For unstructured data like broker notes, the Claude API would be used to extract sentiment and key terms as features. All data and model artifacts would be stored in a Supabase project that you own.

The final system is an API that your CRM calls via webhook. When a deal is updated, the API returns a closure probability score from 0-100 within 200ms. This score is written back to a custom field in your CRM, allowing you to build dashboards and reports. You receive the full Python source code, a runbook for retraining the model, and a maintenance plan.