End-to-End Document AI for Finance, Insurance, Government, and More

Although there are many commercial Document AI APIs on the market, very few are designed to handle segmentation—especially for large, unstructured documents. In many organizations, a single PDF can contain an entire loan archive, a detailed personal profile, or a full medical record. These documents are often hundreds or even thousands of pages long and include a wide range of form types and attachments.

Separating these into individual documents—such as 1003/1008 loan forms, appraisals, verification letters, or lab reports—is not only tedious and time-consuming, but it's also critical to business operations. Manual segmentation is slow, expensive, and prone to errors.

At Softmax Data, we have deep experience developing custom AI solutions that segment long documents with high precision. Our models leverage both textual content and visual cues to intelligently identify document boundaries—even across variable formats and page layouts.

We also build robust preprocessing pipelines to clean and enhance scanned documents through denoising, contrast and lighting correction, automatic rotation, flipping, cropping, and other image processing techniques—laying the foundation for high-quality downstream model performance.

Whether your team works in TensorFlow or PyTorch, we can develop and deliver a custom segmentation model tailored to your data, infrastructure, and compliance requirements.

Extracting data from documents—whether visually structured files like invoices or unstructured formats like contracts—presents unique challenges. Off-the-shelf solutions often struggle with complex layouts, handwriting, or regulatory constraints. At Softmax Data, we’ve built advanced extraction solutions that process both visual and textual information with high accuracy.

We have extensive experience working with commercial APIs such as Google Cloud Document AI and AWS Textract, as well as developing fully custom models, including fine-tuning open-source solutions. Depending on your organization's needs—whether it’s data security, cost efficiency, on-premise deployment, hardware constraints, or regulatory compliance—we tailor our solutions to fit.

Our models can process documents up to 50 pages long, intelligently extracting key fields, tables, checkboxes, and freeform text. We combine computer vision techniques with deep learning models to enhance accuracy, even for scanned or noisy documents.

One of our notable projects was with Rocket Mortgage, where we built an AI-driven extraction system to retrieve critical information used by loan officers for risk assessment and loan approvals. This solution dramatically reduced manual data entry time while improving accuracy and efficiency.

No matter your industry—whether finance, insurance, healthcare, or government—we develop AI-powered data extraction models that meet your exact needs. Our expertise ensures that your documents become structured, searchable, and actionable data, ready for automated processing.

Think of Retrieval-Augmented Generation (RAG) as a virtual domain expert—one that knows your documents inside and out, and is available 24/7 to answer complex questions. Unlike traditional search systems that return a list of documents or keyword matches, RAG systems are designed to generate complete, context-aware answers by retrieving and synthesizing information across multiple sources.

RAG offers several advantages over both conventional search engines and large language models (LLMs) used in isolation. It enables users to get comprehensive answers without needing to click through pages of content. More importantly, unlike generic LLMs that are prone to hallucinations, RAG systems are grounded in your organization's data—ensuring that every answer is accurate, traceable, and trustworthy.

We’ve been building custom RAG systems since 2023 using frameworks like LlamaIndex and Ollama. In one project for Rocket Mortgage, we deployed a RAG pipeline that not only retrieved relevant content from complex financial documents, but also interpreted structured tables across savings, checking, and investment statements. The system then used SQL queries on the extracted data to answer detailed questions such as, “What is the ending deposit as of March 2025?”—dramatically reducing the time loan officers spent reviewing documents.

In another use case, we helped a manufacturing client deploy a RAG system that ingested large volumes of technical documentation and training manuals, updated daily. This allowed technicians to ask questions like, “What’s the calibration procedure for sensor model X?” or “Where is the latest safety protocol for this machine?”—and receive instant, reliable answers based on the most recent materials. This solution increased productivity, reduced errors, and minimized onboarding time without disrupting existing workflows.

Our RAG solutions go far beyond out-of-the-box tools. We integrate document segmentation, classification, and table extraction to ensure your assistant can handle even the most complex files. Whether you're in finance, insurance, manufacturing, or government, we can design and deploy a tailored RAG solution that meets your technical requirements and business objectives—securely and at scale.