AI for Asset Management

AI automates dealmaking workflows for institutional CRE investment teams: sourcing, underwriting, and portfolio analysis.

Agentic AI for financial variance detection and root-cause diagnosis; conversational AI to gather on-site context; AI-generated executive performance summaries.

AI-powered document type detection and field extraction from unstructured real estate documents; AI rent comps engine using public data and tenant sentiment.

ML-powered knowledge graph unifies financial, operational, and third-party real estate data across 3.3B+ addresses. Entity resolution algorithms match properties across disparate sources. Agent.STUDIO platform enables custom AI agents on connected data.

Automated discovery algorithms run every 15 minutes, analyzing 2,000-3,000 attributes per property across 100+ data sources (MLS, iBuyers, off-market, auctions). ML models match properties to investor buy-box criteria and generate predictive pricing, cap rate, and appreciation scores.

Entity resolution ML and a proprietary knowledge graph (Reonomy ID) link 54M+ commercial properties across 3,100 county databases. NLP on corporate filings pierces ownership through shell LLCs to identify beneficial owners. Predictive models generate likelihood-to-sell scores from historical transaction patterns.

ML algorithms process WiFi network data to determine real-time occupancy patterns without hardware sensors. Vector-based similarity search powers a conversational AI interface for natural language queries on space utilization. AI lease abstraction extracts and structures key terms from lease documents.

ML trained on thousands of rent roll formats combined with NLP for entity extraction and OCR for scanned documents. Automatically captures, standardizes, and validates rent roll data across any format, language, or currency using an 800-point data schema.

LLM-powered digital coworker agents pre-trained on CRE workflows automate rent roll parsing, OM abstraction, investment summary drafting, comp compilation, and location narrative generation. Built on technology that underwrote 50,000+ CRE transactions at Stablewood.

Proprietary AI-powered web crawlers collect daily unit-level data from 35M+ apartments. ML cleans, verifies, and parses listings to detect renovations and extract amenities. Maven AI automates concession analysis and renovation ROI.

Machine learning models analyze anonymized mobile device signals to estimate foot traffic, visitor demographics, trade area boundaries, and cross-shopping behavior with 92% property-level accuracy.

Agentic AI and LLMs process unstructured building data (utility bills, assessments, PDFs) to identify retrofit opportunities, rank them by ROI, generate capex strategies, and automate compliance reporting for GRESB, TCFD, and SFDR.

ML-powered AVMs trained on millions of property records and economic indicators achieve 3.1% median error. Image recognition neural networks identify room types and assess condition from photos. CanaryAI is a generative AI assistant for 136M+ properties.

Deploys autonomous AI agents that continuously audit leases for discrepancies and missed charges, automate due diligence by reviewing thousands of leases during acquisitions, and monitor delinquency patterns.

Deep learning computer vision models analyze property photographs to auto-detect and classify 700+ property features including room types, materials, and damage conditions with up to 99% accuracy. Maps outputs to 450+ RESO-standardized data points.