Lineate helps insurance tech companies accelerate claims and policy operations by integrating AI into human decision workflows. We specialize in custom Intelligent Document Processing (IDP) solutions that turn messy, unstructured documents into structured data, enabling faster, smarter, and more accurate decisions across the insurance lifecycle.
We have been solving complex data challenges for over 25 years. We understand every aspect of AI, and our expertise spans ML, LLMs, NLP, and OCR. We’ve built policy advisors for fintech companies, IDP platforms for healthcare and education, and intelligent AI assistants. We specialize in developing and integrating solutions that enhance human decision-making through AI and automation.
A fully customized IDP solution designed for Insurance workflows, including:
We embed intelligent document processing directly into your insurance workflows, focusing on the following key capabilities:
We helped a fast-growing online K–12 school network overcome slow, error-prone enrollment processes driven by manual document verification. Their teams were spending hours cross-checking birth certificates, IDs, and proof-of-address documents against Salesforce records, creating significant delays as volumes grew.
We designed and implemented a two-layer, AI-powered document processing system. The first component operates in the background, automatically classifying documents, extracting key fields, and validating data against application records before writing results directly into Salesforce. The second component, embedded within Salesforce, enables staff to instantly review, verify, and approve critical information through a unified interface.
Hosted on AWS with SSO, the system now delivers fast, accurate, and scalable enrollment, cutting manual work and improving turnaround time.
Banks and credit unions using Our Fintech clients platform needed a faster, smarter way to adjust credit, fraud, and pricing rules. Manual updates required technical teams, lacked real-time insights, and often reacted to issues after they impacted approval rates or fraud losses.
We built an AI-powered Policy Optimizer embedded in the Product Configuration Module (PCM). The system analyzes performance data and recommends rule changes directly to business users, without developer involvement. Teams can accept, adjust, or test updates in real time within their existing workflow.
Financial institutions now optimize policies in days instead of weeks, improve approval rates while reducing risk, and detect fraud patterns earlier, shifting from reactive rule management to a proactive, data-driven approach.
Our client’s job-seeking platform receives resumes in a wide range of free-form formats, making structured evaluation and visualization difficult.
We built a robust resume parser and document viewer to extract key applicant information, such as experience, skills, and education, from unstructured text. We also implemented visual document analysis, enabling resumes to be displayed as interactive documents with selectable regions and word-level metadata based on the original layout.
This streamlined the resume evaluation process and created a scalable interface for recruiters to search, highlight, and interact with applicant data both visually and semantically.
Medical documents related to clinical trials arrived in inconsistent formats, with complex tables, nested values, and varying terminology, making it difficult to extract structured data for compliance and analysis.
We built a document processor capable of ingesting files in a wide range of formats. Powered by an AI engine, the system identifies which fields correspond to specific medical concepts, making highly accurate predictions for name–value pairs, lists, and complex tables. It also includes an interactive feedback layer that allows users to provide input directly on document renderings, helping train the model to improve its predictions over time.
This significantly reduced the administrative burden, improved data accuracy, and ultimately accelerated the overall drug development timeline.
