Kore.ai, an enabler in enterprise generative and conversational AI platform technology, has announced GALE—Generative AI and LLM Platform for Enterprises. This platform enables businesses to build powerful GenAI applications that enhance productivity and innovation and derive value from GenAI.
GALE is the industry’s first dedicated GenAI application platform that addresses businesses’ need to rapidly develop and deploy advanced AI applications and scale them in an enterprise environment. With a no-code toolset and intuitive visual interface, GALE offers enterprises the most efficient way to experiment with multiple models and prompts, build and deploy AI agents for the first time, making it easy for developers without specialized AI talent.
According to a recent McKinsey report, organizations are rushing to deploy GenAI across business units but are hampered by implementation challenges. Companies struggle to find the right use case or fail to scale the AI solution to derive business value. They also lack tools that enable their existing teams to experiment with the best-performing foundational models and responsibly manage AI to ensure safety.
The launch of transformer models has taken the world by storm, leaving enterprises wondering where to start and how to leverage advanced AI for delivering business value,” said Kore.ai CEO and founder Raj Koneru. “GALE addresses the fragmentation and limitations within the current ecosystem. It’s a cohesive platform integrating core AI app-building functionalities with built-in guardrails. None of the hyperscalers or other GenAI app providers offer customers an end-to-end capability to experiment with a range of LLM or SLM models to develop, deploy, and manage sophisticated GenAI apps. GALE does.”
Powered by the Kore.ai platform, GALE simplifies AI application development and accelerates GenAI adoption for businesses, slashing development time up to 50% and enabling rapid prototyping. It integrates into the enterprise ecosystem, ensuring smooth incorporation with business systems and supporting large-scale AI implementations.
- No-code flow-builder and drag-and-drop interface enables creation of complex workflows, apps and AI agents quickly, reducing dependency on specialized technical resources.
- Model Hub provides businesses with centralized access to over 30 AI models (community, open-source or fine-tuned), for refining and fine-tuning them to suit specific business needs.
- AI Agents automate complex tasks and workflows, increasing operational efficiency and allowing businesses to focus on strategic initiatives.
- Prompt Studio offers a playground for developers to craft the right prompts, allowing businesses to move from ideas to production faster.
As enterprise adoption of GenAI picks up pace, organizations are looking to achieve faster time to value from AI initiatives. They need platforms that offer an easy way to experiment with, build, deploy, and scale AI agents and applications,” said Vaibhav Bansal, Vice President, Everest Group. “Typical platform features include AI/ML orchestration layer for efficient model management, the ability to seamlessly connect with enterprise data, a low-code interface, and an application development layer for deploying AI agents or apps. Beyond these, enterprises need access to the best performing foundation models, provision to bring their own model, guardrails to ensure fairness and data security, ability to finetune models on enterprise data, pre-configured GenAI libraries and frameworks for different use cases. Enterprise-grade GenAI platforms that support such features promise an exciting future and have the potential to be the one-stop suite for all AI related needs of an enterprise.”
GALE stands out as an enterprise-grade platform agnostic to LLM models, cloud infrastructure and applications, offering customers maximum choice and flexibility in deploying AI. With prebuilt templates and out-of-box integrations, it triggers massive, enterprise-wide automation through GenAI use cases. Companies can transform their business processes, from front-office to back-office, by building AI agents targeting a specific task or creating complex agentic workflows that automate tedious, time-consuming jobs across the organization.
