Rocket Close Accelerated Mortgage Document Processing by 15x with AWS

Rocket Close demonstrated that even the most labor-intensive part of the mortgage process — document analysis — can be radically accelerated by combining OCR and generative models. Working with AWS Generative AI Innovation Center, the company built a system using Amazon Textract and Amazon Bedrock that increased processing speed by 15x and achieved approximately 90% combined accuracy in document segmentation, classification, and field extraction. For the mortgage business, this is no cosmetic improvement.

A single package may contain questionnaires, statements, income verification letters, disclosure forms, and other documents in the form of scans, PDFs, and photos. They have different structures, quality levels, and completion formats, so a significant portion of time is spent not on decision-making, but on locating relevant pages, recognizing text, and transferring key data into working systems. This is precisely where delays and manual errors typically accumulate.

The Rocket Close solution breaks the task into several clear stages. Amazon Textract handles OCR: it extracts text and structure from documents, including tables, forms, and poorly prepared scans. Next, Amazon Bedrock deploys foundation models for more complex logic: it's not enough to simply read a page—you need to understand what type of document it is, which package it belongs to, where a new section begins, and which fields are truly important for further processing.

This stack transforms a stream of heterogeneous files into a more predictable and machine-readable process. The key here is not just speed, but also the level of automation. The claimed 15x improvement in processing time means employees can move through large cases faster, and part of routine verification moves out of manual mode.

The approximately 90% overall accuracy figure is also important: it's not about a single narrow metric, but three functions simultaneously—document segmentation, classification, and field extraction. For corporate workflows, this is far more valuable than a standalone strong OCR without contextual understanding. This is especially critical in scenarios where a single missed or misrecognized document can send an application back and restart the entire approval chain.

AWS Generative AI Innovation Center's role deserves special mention. Such projects rarely boil down to a simple API call to a model. You need to select an architecture, break the process into stages, determine where classic OCR is sufficient and where reasoning capabilities are needed, and then align all this with quality and operational resilience requirements.

Partnership with the AWS team, judging by the results, allowed Rocket Close to move faster from concept to a system applicable in real document workflows, not just pilot scenarios. For the market, this is yet another example of how generative AI is shifting from flashy chat interfaces to quiet but costly back-office processes. In mortgage lending, the cost of delays is especially visible: the longer a package moves through the approval chain, the higher the burden on the team and the worse the customer experience.

If documents are processed faster and more reliably, companies gain not only time savings, but also more predictable deal timelines, fewer manual handoffs between teams, and better control over data quality. The conclusion is simple: the value of GenAI in financial services is increasingly determined not by flashy demos, but by how well it tackles routine tasks in narrow operational bottlenecks. The Rocket Close case shows that combining OCR and foundation models already delivers measurable results where manual processing once required hours.

For companies with high document throughput, this is a signal to look beyond chatbots and examine internal processes where automation delivers direct operational impact.