Elevate Your Commercial Real Estate Insights with AI

Syntora addresses the challenge of unstructured CRE data by partnering with you to engineer custom NLP solutions. Our engagement would begin with a detailed discovery phase to audit your specific document types such as lease agreements, broker reports, or valuation summaries and understand the exact data points you need to extract. We would then design a tailored technical architecture.

A typical system would involve a document ingestion pipeline, where various file formats are processed and converted. We've built document processing pipelines using Claude API for financial documents, and the same pattern applies to CRE documents, allowing for sophisticated text understanding and entity extraction. The core logic for custom model development and information retrieval would be implemented using Python, leveraging frameworks like FastAPI for efficient API exposure of the extracted insights. Data storage for processed documents and extracted insights would be secured using solutions like Supabase or other enterprise-grade databases, depending on your existing infrastructure and compliance needs.

The delivered system would expose a programmatic interface (API) for your internal tools or a user interface for direct interaction, enabling your team to query, analyze, and act on previously inaccessible information. This bespoke approach ensures the solution is precisely aligned with your operational workflows and strategic objectives, transforming raw text into a tangible competitive advantage. We would define clear deliverables at each stage, from initial proof-of-concept to a deployed, production-ready system. Typical build timelines for this complexity range from 12-24 weeks, depending on the number of document types and extraction targets. Your team would need to provide example documents, access to relevant subject matter experts, and definitions of key data points for extraction.