Chris Mahl is the president and CEO of Pryon. Chris has over twenty years of experience in some of the world’s most famous enterprise software companies, so he specializes in the development of market and operational strategies of technology companies at various growth stages.
Pryon provides a trusted, secure and validated way to implement generative AI in enterprises. Pryon’s best intake and search engine can be paired with Generative LLMS to enable the generation of search results and safely deliver accurate, instant and verifiable answers on the enterprise-wide scale.
Pryon Rag Suite uses industry-leading search technology to securely extract answers in all forms of content, including audio, images, text and video and video, and store answers in countless sources. Pryon’s products are intuitively used, can be accessed from the API via any system, and can be deployed on-site for weeks on the cloud or on-premises.
Pryon focuses on retrieval augmented generation (RAG). Can you explain how your search method differs from other AI-driven search and knowledge management systems?
Pryon’s search method stands out because our search engine is able to access content in real time from a variety of sources such as PDFs, images, web pages, and videos, while maintaining data privacy without external dependencies. We combine semantic search with particle data attribution to achieve more than 90% retrieval accuracy. Unlike many systems, our large organizations are efficiently scaled, enabling teams to make fast, precise decisions based on existing knowledge bases.
Pryon ingested engine is designed to build a large number of multimodal content. What makes your ingestion process unique and how to improve retrieval accuracy?
Pryon’s intake can handle multimodal content – extract answers from audio, images, text, and video from a variety of sources. This solves the basic problem of enterprises disconnecting data. As unstructured data grows by more than 50% per year, our ingestion engine translates dispersed information into structured viable knowledge. The process is designed for security and privacy, protecting sensitive enterprise data while being instantly useful.
Your search engine promises instant, accurate and verifiable answers. How does Pryon ensure accuracy and minimize hallucinations when extracting information?
Pryon ensures accuracy and minimizes hallucinations through a variety of mechanisms. Our technology combines semantic search with granular data attribution, meaning that answers can be traced back to their specific sources. This attribution is critical to verification. The system accesses content in real time from the original source, rather than relying on a knowledge base that may be outdated or incomplete. This direct connection to the original material, coupled with our high retrieval accuracy (over 90%), greatly reduces the risk of hallucination that plagues many generative AI systems.
How does Pryon handle real-time updates of information, especially in dynamic environments such as government, energy and healthcare?
Pryon ensures real-time access to the latest information with flexible on-demand content synchronization. Users can trigger content synchronization based on our Sync-API as scheduled, or use our Sync-API to automatically update (weekly, daily or even hourly) based on operational requirements. Our delta inspection process optimizes efficiency by updating only changes, ensuring fast, accurate and resource-effective knowledge retrieval in mission-critical environments such as government, energy and healthcare.
Pryon works with governments and defense agencies. While details are often classified, can you discuss use cases where your AI can significantly improve decision-making or operational efficiency?
Pryon works with a range of defense and intelligence agencies, including the Air Force Research Laboratory (AFRL) and the Chief Digital and Artificial Intelligence Office (CDAO) to help simplify operations and enable faster and more informed decision-making.
A powerful example is our collaboration with the US Air Force Office of Digital Transformation (DAF DTO). The team supports acquisitions and maintenance personnel, who often need to find key information buried in hundreds of thousands of web pages and files. Together, we launched DTO Wingman, an AI-powered assistant that provides accurate real-time answers to complex questions, combined with source attribution.
Instead of manually searching for policy files or regulations, users can simply ask questions like “What do I have the right to buy with a travel card?”. or “What is digital building code? How does it relate to acquisitions?” AI returns precise responses and even helps to quickly generate reports and demonstration materials.
By enabling Air Force and space personnel to get trustworthy answers immediately, DTO Wingman is helping teams work more effectively and provide reliable, timely guidance to senior staff and decision makers.
Your life science work mentions AI-assisted research. How does Pryon’s system help researchers navigate in large sets of data such as PubMed or private research repositories?
Pryon’s system helps researchers browse large data sets or private research repositories with several key features.
Enhanced research quality:
- Reduce human error: Systematic search of the latest data ensures a small number of articles or evidence that has been ignored.
- Supported by evidence: Each answer is based on the original literature, thus facilitating data-driven conclusions and returning to its sentences.
Protect highly sensitive content:
- Confidentiality: Maintain strict access control and data encryption, essential for proprietary or patient-related data sets.
- obey: Through data managed under regulations such as HIPAA or GDPR, researchers can trust that sensitive information is protected.
For customer service and sales, Pryon’s AI is in terms of increasing efficiency and reducing support load compared to traditional chatbots and CRM solutions?
Customer Service/Sales interactions often have to balance the accuracy and flexibility of their chatbot/CRM solutions. Since providing a wrong answer to a customer is unacceptable and may have legal implications, many chatbot providers and traditional conversational AI solutions choose to limit the flexibility of solutions, while the hard “FAQ-only FAQ-only” style interacts with each other.
It’s a pain for suppliers, requiring manual coding of specific answers to common questions and providing customers with a bad experience with chatbot interfaces – but an entirely inflexible experience is hardly different from reading FAQs. Other vendors choose to try to use a more flexible generation experience, with smaller boundaries on LLM, but due to the lack of precise retrieval, this involves tucking the entire product catalog or web page into the context window of the LLM, thus reducing the accuracy of the output and thus the accuracy of the output.
The art and science of rags are about maximizing signals (the truth) and minimizing noise (the environment that often confuses LLM). Pryon Retrieval Accuracy – The ability to get answers at specific sentence-levels in all documents, meaning customer service and sales no longer require compromised precision to be flexible.
What do you think is the biggest challenge for today’s enterprise AI adoption, especially rag-based systems?
While we will certainly find something in our interactions with the market, it is increasingly recognized that “AI-Ready Data” (or lack) is the biggest failure point in AI deployment.
- 91% of executives in the Harvard Business Review survey said a reliable data foundation is crucial to successful AI deployments.
- McKinsey found that 70% of Genai plans face data-related challenges, and only 1% of enterprise-important data are reflected in today’s models.
- The Wall Street Journal has made reliability the number one concern for AI agent adoption, which is closely related to data quality and accessibility.
- Gartner determined that the lack of Genai Ready data was the main reason for deployment failure.
AI-READY data doesn’t just not vectorize Word documents – it’s about unifying orphaned sources, using complex formats such as multi-modal input, cleaning data, enhancing data, making it a format LLM can be used, can be used at the right granularity to maintain optimal accuracy and optimal cost, and stable cost, and stable state and stable state.
These are huge challenges that require dedicated capabilities and tools – in a survey of intra-enterprise development solutions running by Pryon, data preparation ranks the most expensive, time-consuming, and technically challenging part, followed by information retrieval.
How do you distinguish Pryon’s RAG Suite from the enterprise solutions offered by Microsoft, Google, or OpenAI?
Specific differences vary by player, but at a high level, large tech players focus on the “interface” of AI at work. Pryon focuses on a more basic level of the stack – the knowledge layer. Pryon solves the profound problems of data preparation and retrieval, while large technicians work on providing a wide range of AI solutions that can serve some simple rag use cases, but often crash with the real-life complexity of enterprise and government use cases. Pryon can also be commensurate with these systems, while Copilot, Gemini, or GPT generated content is inserted into the Pryon knowledge layer and ready for use by downstream applications and agents.
As AI regulations develop, such as the EU AI Act and US AI Guidelines, how does Pryon use compliance and ethical AI?
As AI regulations develop globally, Pryon remains committed to compliance and ethical AI deployment. Our approach aligns with frameworks such as the EU AI Act, U.S. AI Guidelines and Department of Defense Head AI (RAI) Principles to ensure our AI solutions are trustworthy, transparent and manageable. By complying with the RAI Shield framework, we integrate rigorous evaluation, traceability and continuous monitoring of the AI lifecycle and improve security, equity and performance. By embedding these best practices into our deployment approach, Pryon empowers organizations to responsibly leverage AI while achieving the highest regulatory and ethical standards.
Thanks for your excellent interview, and readers who hope to learn more should visit Pryon.
