Real estate finance businesses can save significant time and reduce manual labor costs by automating the processing of complex documents.
What is Built Technologies’ AI document intelligence engine and what changed?
Built Technologies, a real estate finance software provider that processes over $500 billion in real estate projects, deployed an AI-powered document processing engine on Amazon Bedrock and the AWS Intelligent Document Processing Accelerator.
Built partnered with the AWS Generative AI Innovation Center, AWS Partner AND Digital, and AWS account teams to create the engine, and that engine now serves as the foundation for agentic products across the real estate lifecycle. which classifies, splits, extracts, evaluates, and reasons over complex real estate finance documents. Classification and extraction workflows that previously took 3 to 9 days now finish in minutes per package.
Built turned document processing from a multi-day manual workflow into a minutes-long automated pipeline.
What is the evidence behind Built Technologies’ document engine?
The engine supports more than 250 document types and is scaling to a production workload of 20 million documents per month.
The stated throughput includes 300,000 documents per week and batch processing runs over 50,000 documents, while individual documents can exceed 500 pages. Built requires over 95% confidence in classification and extraction workflows, and any result below that threshold routes to a human reviewer with page-level evidence attached. The first production use case was commercial construction loan draw packages, where borrowers submit large, variable document collections to request fund disbursements. Users can upload examples of a new document type and Amazon Bedrock generates a proposed extraction schema, which subject matter experts refine, test, and version without an engineering project.
The evidence is production-grade: 250+ document types, a 95% confidence bar, and millions of documents moving monthly.
How does Built’s engine compare to the alternatives, and what background do small business owners need?
Built previously ran 26 processors built on OCR and traditional machine learning, an approach that only worked where fields were explicit and layouts were predictable.
The new pipeline runs OCR through Amazon Textract, classifies and splits documents with Amazon Bedrock, then extracts against schemas that experts can generate from sample uploads. Teams route straightforward documents like standard invoices to smaller, faster models such as Amazon Nova Lite, and send complex loan agreements or offering memorandums to larger models like Anthropic Claude through Bedrock. When a 150-page draw package splits into sections, each section extracts in parallel, so total processing time is bounded by the longest section rather than the sum of all sections. A rule-validation workflow then checks documents against plain-language business questions and returns a determination of compliant, non-compliant, or insufficient evidence, with citations back to the exact supporting sections. Static classification instructions get cached and reused across requests, which keeps costs sane when a single processor handles a dozen or more document classes.
The shift is from template-based extraction to document understanding that reasons over content.
How does Built’s document engine affect day-to-day operations for small businesses?
If you run a lending, brokerage, or property operation, document review stops being the bottleneck that stalls your deals.
The same foundation powers draw review agents that classify package contents, identify missing documents, and extract invoice and lien waiver data, plus insurance agents that validate certificates and compliance agents that inspect permits and reports. Reviewers work in a split-pane interface with bounding-box overlays showing where each value was found, and their corrections feed back into schemas, prompts, and evaluation datasets, so the system improves with every fix. We cover similar shifts in our document automation signal coverage.
Operations move from manual data entry to exception handling.
The closing binder hits the title office desk at 4:55 PM on a Friday, 500 pages of draw requests, lien waivers, and insurance certificates stacked in no particular order. Your processor used to block off 3 to 9 days to classify every page, key in the amounts, and chase missing documents while the borrower called twice a day for updates. Now the same package uploads in one shot, the engine splits it into labeled sections, extracts the fields, and flags 1 illegible dollar amount for human review before your coffee cools. Your staff stops retyping documents and starts clearing the exceptions the machine marked, which means the deal closes while your competitor’s file is still sitting in a queue.
What is the final verdict on Built’s document engine?
Built and AWS delivered a production-scale document intelligence engine that was validated through large batch processing runs and production-scale testing.
The published outcomes include 3 to 9 day workflows compressed to minutes, support for more than 250 document types, and an architecture scaling to 20 million documents per month. More than 300 of the top financial institutions trust Built, and the human-in-the-loop design keeps expert oversight on low-confidence outputs instead of removing people from the loop.
Accuracy with oversight beats volume without it.
Source: AWS Machine Learning Blog
