Infinia ML automates document understanding.
Infinia ML uses machine learning to automate document workflows that once required human understanding. For claims, proposals, contracts, and more, the Infinia Intelligent Document Processing (IDP) platform can:
- Find and extract key information
- Organize ideas
- Standardize phrases
- Improve internal search
Businesses license the Infinia IDP platform to streamline internal processes and make predictions on the newly-processed documents. Enterprise leaders, technology innovators, and government partners trust Infinia ML on critical projects with sensitive data.
Use cases include:
- Extracting key data from health claims and procedures
- Sorting contracts into different categories
- Standardizing job titles across different companies
- Finding new opportunities based on documented past successes
The document processing platform works with PDF, IMG, TXT, and other files. The platform applies natural language processing together with other technologies like advanced optical character recognition and custom machine learning. Infinia IDP grasps not only words, but the meaning embedded in a page’s layout and structure.
The initial system output can be text, tables, form fields, and more. Infinia ML data scientists make business-specific adjustments to optimize the platform for each use case.
Seamless deployment occurs via secure Infinia ML cloud with RESTful API or on-premises with Docker/Kubernetes.
After deployment, Infinia ML’s continuous audit solution keeps the system on track. A dashboard and regular reports help maintain performance by flagging data and operational issues.
Focus
Portfolio
Healthcare Case Study
Client: SignalPath
Challenge: Understand complex medical language and extract relevant data for processing.
Solution: Data pipelines and machine learning models leveraging OCR & NLP to pull content from large unstructured documents containing paragraphs, images, tables, and footnotes.
Deployment: In-product implementation.
Read more: https://go.infiniaml.com/extracting-complex-medical-language