Seekr Unveils SeekrFlow: A Comprehensive Platform for Trustworthy Enterprise AI

Seekr debuts SeekrFlow platform for training and deploying trustworthy enterprise-ready AI - SiliconANGLE

Seekr debuts SeekrFlow platform for training and deploying trustworthy enterprise-ready AI Seekr Technologies Inc., an enterprise-ready artificial intelligence platform building trust into the application lifecycle, today announced the launch of its self-service AI product SeekrFlow that will allow enterprise customers to train, validate, deploy and scale up AI apps. As more companies embrace AI technology in their businesses, many have discovered that projects that integrate AI and machine learning can be complex and difficult to piece together. Seekr claims that it helps simplify this process by providing a single source. SeekrFlow manages everything from a single application programming interface call, software development kit and starting today an intuitive no-code user interface. According to the company, businesses can start from scratch and build up a production-grade large language model in 30 minutes or less, ready to validate and deploy. The same system allows businesses to train their model with a system the company calls "Principal Alignment," an intelligent agent that simplifies maintaining the model's alignment with domain-specific knowledge, such as company policies, industry-specific regulations and brand guidelines. The feature maintains the accuracy of the base model responses by up to 3x and 6x respectively and at a 90% reduced data preparation cost and 2.5x faster than traditional methods, the company said. "Many enterprise AI projects today have been stalled due to complexity, cost, and hallucinations," said Seekr President and Chief Technology Officer Rob Clark. "SeekFlow addresses all of those concerns, and by being platform and hardware agnostic, makes it available no matter where the customer runs AI or where their data resides." To tackle the problem of hallucinations and accuracy, SeekrFlow provides enterprise customers tools to look inside models, contest the results and validate them at the token level. Using confidence scores, users can troubleshoot by having the model critique its outputs and provide scores between 1-100. Color coding helps users easily identify and examine individual tokens and pinpoint where further validation is needed, including side-by-side comparisons for prompts between different models for real-time evaluations. Of course, once a model is launched, it's not over. SeekrFlow provides monitoring of LLM health and performance while in production through a visual dashboard with real-time visibility. The backend delivers metrics including uptime, API calls, memory usage and token counts so that developers, engineering teams and other operators can easily see what's happening at a glance. This ensures users can rapidly scale resources and optimize for cost when needed. SeekrFlow is AI model agnostic, which means that it will work with virtually any open or closed source large language model that the customer wants to bring, including OpenAI's GPT-4, Meta Platform Inc.'s Llama-3, Mistral AI Mixtral and more.

Seekr announces end-to-end SeekrFlow platform for training and deploying trustworthy enterprise-ready AI - SiliconANGLE

Seekr announces end-to-end SeekrFlow platform for training and deploying trustworthy enterprise-ready AI Seekr Technologies Inc., an enterprise-ready artificial intelligence platform building trust into the application lifecycle, today announced the launch of its self-service AI end-to-end product SeekrFlow that will allow enterprise customers to train, validate, deploy and scale AI apps. As more companies embrace AI technology in their businesses, many have discovered that projects that integrate AI and machine learning can be complex and difficult to piece together. Seekr claims that it helps simplify this process by providing a single source. SeekrFlow manages everything from a single application programming interface call, software development kit and starting today an intuitive no-code user interface. According to the company, businesses can start from scratch and build up a production-grade large language model in 30 minutes or less, ready to validate and deploy. The same system allows businesses to train their model with a system the company calls "Principal Alignment," an intelligent agent that simplifies maintaining the model's alignment with domain-specific knowledge, such as company policies, industry-specific regulations and brand guidelines. The feature maintains the accuracy of the base model responses by up to 3x and 6x respectively and at a 90% reduced data preparation cost and 2.5x faster than traditional methods, the company said. "Many enterprise AI projects today have been stalled due to complexity, cost, and hallucinations," said Seekr President and Chief Technology Officer Rob Clark. "SeekFlow addresses all of those concerns, and by being platform and hardware agnostic, makes it available no matter where the customer runs AI or where their data resides." To tackle the problem of hallucinations and accuracy, SeekrFlow provides enterprise customers tools to look inside models, contest the results and validate them at the token level. Using confidence scores, users can troubleshoot by having the model critique its outputs and provide scores between 1-100. Color coding helps users easily identify and examine individual tokens and pinpoint where further validation is needed, including side-by-side comparisons for prompts between different models for real-time evaluations. Of course, once a model is launched, it's not over. SeekrFlow provides monitoring of LLM health and performance while in production through a visual dashboard with real-time visibility. The backend delivers metrics including uptime, API calls, memory usage and token counts so that developers, engineering teams and other operators can easily see what's happening at a glance. This ensures users can rapidly scale resources and optimize for cost when needed. SeekrFlow is AI model agnostic, which means that it will work with virtually any open or closed source large language model that the customer wants to bring, including OpenAI's GPT-4, Meta Platform Inc.'s Llama-3, Mistral AI Mixtral and more.