Named Entity Recognition
Identify people, locations, and organizations within a body of text.Explore
Primer acquires Yonder, adds disinformation analysis to AI portfolio for information operations.READ MORE
Primer provides industrial-grade NLP applications for government agencies, financial institutions, Fortune 50 companies, and many other organizations.
Assemble your own NLP solutions with our world-class pre-trained engines. Our rigorously trained engines are performant, scalable, and fast. They come with an API to quickly integrate with your unique applications and workflows, both in the cloud and on-premises. With our engines at work, you can instantly and confidently scale your business and operations.
Encode your knowledge into machine learning models that can automate text-based workflows at scale with human-level quality. Build your own models from scratch, retrain our world-class models for your specific task, or use Primer models off-the-shelf. Anyone in your organization can build and train models using Primer Automate — no coding or technical skills required.
Add a structured layer of intelligence on top of your data and create a scalable, self-curating knowledge base that can sift through billions of documents in seconds. Find answers to critical questions quickly, monitor updates in real time, and automatically generate easy-to-read reports.
Primer’s leading data scientists are constantly updating and developing new state-of-the-art Engines to leverage enterprise-ready capabilities across verticals and use cases. Build your custom NLP pipeline with these engines:
80% of the world’s most valuable open-source data that change agents leverage throughout the course of breaking events sits untapped. Artificial intelligence (AI) helps transform zettabytes of information into Open Source Intelligence (OSINT) that the DOD can use to gain real-time intelligence for situational awareness.
Alliance Trucking acts as a broker between truck owner-operators and construction companies who need loads of construction material delivered. They needed to be able to feed emails directly into an #NLP model that could understand RFQs and produce precise estimation criteria. Accurate data labeling was key.