- Seekr Technologies launches SeekrFlow, a self-service AI platform.
- Enables enterprises to train, validate, deploy, and scale AI applications in under 30 minutes.
- Combines a single API, software development kit, and a no-code user interface.
- Principal Alignment feature increases model accuracy up to six times and reduces data preparation costs by 90%.
- Tackles AI complexity, cost, and hallucinations with platform-agnostic design.
- Provides tools for real-time model validation and comparison, enhancing troubleshooting.
- Offers real-time monitoring of model performance through a visual dashboard with key metrics.
- Supports various LLMs such as GPT-4, Llama-3, and Mixtral.
- Partnership with Intel enables scalable AI deployment on Intel Tiber Developer Cloud.
Main AI News:
Seekr Technologies Inc. has introduced SeekrFlow, a self-service AI product designed to simplify AI integration for enterprises. The platform enables businesses to train, validate, deploy, and scale AI applications efficiently through a single API, a software development kit, and a no-code user interface. This solution allows enterprises to create and deploy production-grade large language models in under 30 minutes.
As companies increasingly adopt AI, many face challenges with the complexity of integration. SeekrFlow addresses this by offering a streamlined platform that manages the entire process. A key feature, “Principal Alignment,” helps organizations align their models with industry regulations, internal policies, and brand guidelines. This enhancement increases model accuracy by up to six times, reduces data preparation costs by 90%, and speeds up the process by 2.5 times compared to traditional methods.
SeekrFlow also tackles common AI issues such as cost, complexity, and hallucinations. Its platform-agnostic nature ensures that it works regardless of where the AI is deployed or where the data is stored. The product includes tools that allow users to inspect and validate AI outputs at the token level, using confidence scores to identify areas requiring further validation. Color-coded tokens and side-by-side model comparisons enable quick real-time evaluation and troubleshooting of model performance.
After deployment, SeekrFlow monitors AI model health and performance with a visual dashboard that provides real-time metrics such as uptime, memory usage, and token counts. It ensures that teams can scale resources as needed and optimize costs efficiently.
The platform’s model-agnostic design supports various open- or closed-source large language models, including GPT-4, Llama-3, and Mixtral. SeekrFlow can also integrate with any hardware or AI infrastructure. Recently, Seekr entered into a multiyear collaboration with Intel, allowing businesses to deploy AI models through the Intel Tiber Developer Cloud for trusted, scalable AI solutions.
Conclusion:
SeekrFlow’s introduction to the market marks a significant advancement in enterprise AI integration. By simplifying the deployment process and reducing the complexity of AI projects, SeekrFlow positions itself as a critical tool for businesses leveraging AI technology at scale. With its model-agnostic and hardware-agnostic design, the platform’s flexibility ensures broad adoption potential across industries. Moreover, the collaboration with Intel signals a trend towards more accessible, scalable, and trusted AI solutions for enterprise-level deployments. It could drive increased competition among AI service providers, pushing the market towards more streamlined, cost-efficient solutions with enhanced model accuracy and real-time monitoring capabilities.
