Rocket Close cuts mortgage document processing time by 15x with AWS

Rocket Close has shown that even the most labor-intensive part of the mortgage process, document review, can be sped up dramatically by combining OCR with generative models. Working with the AWS Generative AI Innovation Center, the company built a system on Amazon Textract and Amazon Bedrock that made processing 15x faster and brought combined accuracy for segmentation, classification and field extraction to roughly 90%. For a mortgage business, that is not a cosmetic improvement.

A single package can contain applications, bank statements, income verification, disclosure forms and other documents arriving as scans, PDFs and photos. They vary in structure, quality and how they were filled out, so much of the time goes not into making decisions but into finding the right pages, recognizing text and copying key data into business systems. This is the stage where delays and manual errors pile up most often.

Rocket Close's solution splits the job into several clear stages. Amazon Textract handles OCR, extracting text and structure from documents, including tables, forms and poorly prepared scans. Amazon Bedrock then brings in foundation models for the harder logic: the system has to do more than read a page.

It needs to understand what kind of document it is, which package it belongs to, where a new section starts and which fields actually matter for downstream processing. This stack turns a stream of mixed files into a more predictable, machine-readable process. The key point here is not just speed but the level of automation.

The claimed 15x time reduction means staff can work through large cases faster, and some routine checks no longer have to be done by hand. The roughly 90% overall accuracy figure matters too: it covers not one narrow metric but three functions at once — document segmentation, classification and field extraction. For an enterprise workflow, that is far more useful than a strong standalone OCR engine with no grasp of context.

This matters most where a single missing or misread document can send an application back and restart the whole approval chain. The role of the AWS Generative AI Innovation Center deserves its own mention. Projects like this rarely come down to a simple model call over an API.

The team has to choose an architecture, break the process into stages, decide where classic OCR is enough and where reasoning is needed, and then tie it all to quality and operational resilience requirements. Judging by the results, the partnership with the AWS team helped Rocket Close move faster from idea to a system that can be used in real document workflows, not just in pilot scenarios. For the market, this is another example of generative AI moving out of demo chat interfaces and into quiet but costly back-office processes.

In mortgages, the cost of delay is especially visible: the longer a package sits in the review chain, the heavier the load on the team and the worse the customer experience. If documents are processed faster and more consistently, the company gains not only time savings but also more predictable closing timelines, fewer manual handoffs between teams and better control over data quality. The takeaway is simple: the value of GenAI in financial services is increasingly defined not by flashy demos but by how well it removes routine work from narrow operational bottlenecks.

The Rocket Close case shows that pairing OCR with foundation models already delivers measurable results where hours of manual processing used to be required. For companies with heavy document workloads, it is a signal to look beyond chatbots to internal processes, where automation has a direct operational impact.