We give developers the tools to build document infrastructure for both sides of the model.
Before the model, Apryse turns documents into LLM-ready structured data. After the model, WebViewer and DOCX Editor put a human in the loop before AI output becomes a decision. Most AI-native teams come to us already past the first problem. Models are running. The second problem, getting a human to trust and act on what they produce, is where we start.

WebViewer and DOCX Editor: Human-in-the-Loop, Built Into the Workflow
Every AI output needs a human checkpoint before it becomes a decision. Apryse WebViewer and DOCX Editor embed directly into your application, so that checkpoint happens inside your product instead of a separate review tool. Annotate, redact, correct, and approve AI-generated output in context, on PDFs, Office files, and more, without leaving your workflow.
Fully Accessible
WCAG-compliant out of the box. Your compliance team is covered before the conversation starts.
Data Pipeline Built to Feed AI
Verification only matters if the data feeding your model was solid to begin with. Most AI pipelines fail at the document layer, not because the models are wrong, but because the data is. Apryse solves this with a two-stage pipeline that runs entirely in your environment.
First, Apryse's deterministic layout engine reads the physical structure of your documents and converts them into a clean, consistent, machine-readable format.
Then purpose-built AI models, developed by Apryse's own research team, interpret what that structure means: classifying document types, extracting key values, and producing AI-ready data your pipeline can actually use.
No generic LLMs. No per-page cloud cost.

Resources
WebViewer in Practice: Tutorials and Customer Stories

How Blue Voice Uses Apryse WebViewer to Deliver AI-Powered Policy Access Across 200+ Police Departments

Juume AI Selects Apryse to Power Document Integrity Behind Its Agentic AI Platform, CapraOne™

AI Powered Redaction from Within Apryse WebViewer

Creating a Web Application that Interacts with AI. Part 1: Setting up the Client-side Interface

Creating a Web Application that Interacts with AI. Part 2: Interacting with the LLM

Creating an MCP Server That Allows an AI Application to Run Your Code
Two Decades in Document Infrastructure
Apryse has spent over 20 years on document problems general-purpose AI wasn't built to handle: human review after the model, extraction accuracy before it, deployed however your security and compliance team requires. The enterprises winning with AI aren't the ones who automated the most. They're the ones who kept humans in control of what mattered. See how Apryse delivers both sides of the model.



