Engines are pre-trained, proprietary models built with Primer’s state-of-the-art NLP (Natural Language Processing), NLU (Natural Language Understanding), and NLG (Natural Language Generation) technology. Engines enable humans to automate their reading and writing tasks at scale with human-level precision and discover insights that were previously impossible to find.

Engines support a wide range of applications. They are used by decision-makers, data scientists, data analysts, subject-matter experts, and operators across government and commercial verticals. What these users have in common is that they need to rapidly derive meaningful insights from massive amounts of text-based data to improve their situational awareness, business processes and products, and operational decision-making.

Engines can be deployed in the cloud or on-premises, depending on the organization’s mission needs and the scale of its data landscape. Primer offers managed cloud hosting to public sector customers, or these customers can choose to host Engines on their own GovCloud or on-premises infrastructure. Following deployment, customers can use Primer’s APIs to quickly integrate the engines into their preferred workflows.

Using its internal team of 60+ Machine Learning engineers, Primer has developed a suite of Engines, built on state-of-the-art transformer model technologies. Primer Engines exceed the performance of competitor models while enabling new tasks and use cases, thanks to our custom modeling approaches and our significant investment in high quality, labeled data for specific use cases.

Primer Engines are trained on diverse data sources by design to minimize bias. Information on the training datasets is available to you for review, and you can reach out to our sales team to learn more.

Yes, Primer Engines are designed to work seamlessly with each other. For instance, Primer‘s Sentiment Analysis engine can efficiently triage and classify a massive cache of documents into those showing positive and negative sentiments. You can deploy the Primer Summarization engine on top of the extracted content to derive summaries of each category.

Primer consistently leverages state-of-the-art security tools and platforms to ensure your hosted data remains safe. To ensure data privacy, Primer only stores data transiently until it completes the request.

Developers can access our API to quickly integrate Engines into your enterprise. The API can be used with a single cURL statement and from any programming language. Primer API does not require any special libraries. Developers can make standard HTTP calls to combine multiple engines.

Any software developer should be able to help integrate Engines into the company’s workflow with Primer’s API documentation. Once you make a call to the API, the service handles the machine learning required to analyze the text.

For starters, you will need to address these basic questions:

• Workflow automation: prioritize the areas for automation where Engines can alleviate workflows and improve processes.
• Input systems: identify the Engines’ data sources and where and how they can be accessed and prepared.
• Output systems: determine what applications the Primer output will integrate with, such as products, dashboards, BI tools, knowledge bases and more.

The Primer Engine API consists of HTTP(s) requests and JSON responses. Requests to Engines are standard HTTP calls that can be made from the command line or using the tools available in your chosen programming language or development framework. The JSON response contains the keys and values that the engine derived for the provided input. For example, if you make a call to the Summarization Engine, the response is a JSON object that contains the key “summary” and the resulting summarization.

Yes, we offer an on-premises solution for Engines. If you select this option, our Solutions Architect will work closely with your representative to design and implement the most appropriate solution for your environment.