Bryckel is the first AI-powered tool designed specifically for real estate teams to manage and extract data from complex documents effectively, mitigating risks and improving insights.
Automates the process of abstracting leases, extracting critical dates, financial obligations, and clause banks in minutes instead of days, tailored specifically for the real estate industry.
Provides AI-driven insights to enhance the performance and strategic capabilities of real estate teams, enabling them to stay ahead in achieving strategic objectives.
Facilitates informed decision-making processes for acquisitions, due diligence, and asset portfolio management by delivering precise document analytics.
Captures key financials accurately and identifies lease exceptions to ensure debt covenant compliance and validate lease data during due diligence.
Allows teams to use AI CRE tools without needing data scientists, simplifying the process of acquiring and utilizing data for real estate operations.
Automates lease management by driving transaction and rent management globally with consolidated data visibility and next-generation insights.
Offers insights by analyzing hundreds of lease documents simultaneously, helping users make informed real estate decisions.
Provides lease abstraction for a variety of languages, including English, Chinese, French, and Spanish.
Allows users to customize their lease abstracts to include specific clauses and information relevant to their needs.
Offers the ability to download abstract results in CSV format or integrate directly with lease administration systems.
Rapidly analyzes full sets of lease-related documents, providing insights into amendments, side letters, and operating expense statements. This ensures real estate teams can manage financial and legal obligations with precision.
Offers AI assistants that provide actionable insights from complex lease documents, helping users to better understand lease terms and changes.
The system provides two types of responses: RAG (Retrieval-Augmented Generation), which are based on specific information retrieved from the lease database, and Non-RAG, which are general responses not directly tied to the lease database.