What happened
Built Technologies, a real estate finance software provider, deployed an AI-powered document processing engine on Amazon Bedrock and AWS Intelligent Document Processing Accelerator. The system classifies, splits, extracts, evaluates, and reasons over complex real estate documents, reducing workflows that previously took days to minutes and supporting over 250 document types.
Why it matters
Real estate finance runs on documents—loan agreements, draw packages, insurance certificates, and dozens more—that are long, inconsistent, and difficult to process with traditional automation. Built processes over $500B in real estate projects annually; automating document understanding at scale directly impacts lending decisions, underwriting timelines, and compliance. The solution shifts from simple text extraction to contextual reasoning—agents can now identify covenants buried in legal language, validate insurance coverage, and surface portfolio risks rather than just pulling labeled fields.
What to watch
Built designed the solution as a reusable horizontal capability shared across multiple agents launching throughout the year, including draw review agents, loan agreement agents, insurance validators, and asset management agents. The company partnered with AWS Generative AI Innovation Center and AND Digital to build the infrastructure, which uses AWS Step Functions to orchestrate a multi-stage pipeline (OCR, classification, extraction, assessment) that processes classified document sections in parallel.
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Real estate finance has traditionally relied on manual document review because the documents are inherently complex: long agreements with nested tables, scanned pages, handwritten annotations, and domain-specific language. Built's previous approach used optical character recognition (OCR) and traditional machine learning to extract data from 26 specific document types, which worked well for structured documents with predictable layouts and explicit fields. However, as the company expanded its AI roadmap across the full real estate lifecycle, this narrow approach hit its limits—the company needed to process more than 250 document types, handle millions of documents, and support agents that could reason about context and meaning, not just pull text from labeled fields.
The shift from extraction to understanding is exemplified by the covenant problem in loan agreements. A keyword search for "covenant" might miss obligations expressed in legal language across multiple sections; traditional extraction models struggle with implicit context and distributed information. An agentic workflow, by contrast, can identify relevant sections, infer which clauses represent covenants even when unlabeled, extract structured outputs with supporting evidence, and route low-confidence results to experts for human review. Built designed its new system as a horizontal capability—a shared foundation for multiple agents—rather than a single-purpose tool, so that draw review agents, loan agreement parsers, insurance validators, and compliance checkers all draw on the same core document intelligence infrastructure.
The technical foundation rests on Amazon Bedrock (for generative AI-powered reasoning) and the AWS Intelligent Document Processing Accelerator, orchestrated through AWS Step Functions. By splitting documents into classified sections and processing them in parallel via AWS Lambda, the system can complete in minutes what previously took days. This architectural choice—parallel extraction within a multi-stage pipeline—is a key reason the speed improvement is so dramatic.
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