
LM-Kit.NET serves as a comprehensive toolkit tailored for the seamless incorporation of generative AI into .NET applications, fully compatible with Windows, Linux, and macOS systems. This versatile platform empowers your C# and VB.NET projects, facilitating the development and management of dynamic AI agents with ease.
Utilize efficient Small Language Models for on-device inference, which effectively lowers computational demands, minimizes latency, and enhances security by processing information locally. Discover the advantages of Retrieval-Augmented Generation (RAG) that improve both accuracy and relevance, while sophisticated AI agents streamline complex tasks and expedite the development process.
With native SDKs that guarantee smooth integration and optimal performance across various platforms, LM-Kit.NET also offers extensive support for custom AI agent creation and multi-agent orchestration. This toolkit simplifies the stages of prototyping, deployment, and scaling, enabling you to create intelligent, rapid, and secure solutions that are relied upon by industry professionals globally, fostering innovation and efficiency in every project.
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SearchUnify is an enterprise Agentic AI platform that connects fragmented enterprise knowledge and deploys autonomous AI agents to handle customer support operations from first contact to resolution. At its core, the platform is powered by SearchUnifyFRAGâ„¢ (Federated Retrieval Augmented Generation), Agentic RAG, and the SCORE (Contextual Relevance Engine) framework, enabling accurate, grounded responses drawn from unified enterprise data.
The product suite spans five core tools. Cognitive Search retrieves contextually relevant answers across distributed knowledge sources. SearchUnifyGPTâ„¢ generates cited AI responses at every support touchpoint. SUVA, the virtual assistant, handles multi-turn conversations autonomously. Knowbler supports knowledge capture, structuring, and surfacing at the point of need. Agent Helper delivers next-best-action recommendations to support agents during live case resolution.
Extending these capabilities, the Agentic AI Suite comprises eight AI agents: AI Support Agent for self-service resolution, AI SupportPlus Agent for autonomous CRM ticket handling, AI Classification Agent for case tagging and routing, AI Escalation Manager for escalation risk prediction, AI Knowledge Agent for automated content creation and gap detection, AI Competency Agent for L2 technical workflow resolution, AI Agent Partner for real-time agent guidance, and AI Case Quality Auditor for evaluating every closed case.
SearchUnify supports BYOLLM for LLM flexibility, Model Context Protocol (MCP) for secure system connectivity, and a governance layer covering role-based access control, PII masking, and ISO 27001, HIPAA, and SOC 2 compliance. The platform connects with 100+ enterprise tools including Salesforce, Zendesk, and ServiceNow.
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Cohere Compass
Cohere Compass operates as an advanced platform for enterprise search and discovery, seamlessly connecting agents and models with business data to provide AI applications with crucial contextual information through powerful search and retrieval functions. By utilizing advanced extraction methods alongside AI-enhanced indexing, it proficiently identifies and delivers relevant insights from enterprise documents with exceptional accuracy. The platform is designed to be multimodal, multilingual, and independent of specific formats, allowing it to interpret a wide range of document types, such as images, presentations, spreadsheets, PDFs, DOCX, and XLSX files. Additionally, it accommodates integration with existing data sources or supports local document uploads, streamlining the automatic processing, indexing, and management of that data without requiring teams to develop or scale their own vector database infrastructure. Leveraging Cohere’s Embed and Rerank retrieval models, Compass not only aids AI agents and retrieval-augmented generation applications but also promotes a unified approach to enterprise knowledge search, thereby boosting collaboration and informed decision-making throughout the organization. The adaptability and effectiveness of this platform make it a critical asset for businesses looking to maximize their data's potential. Ultimately, its capabilities ensure that organizations can navigate their vast data landscapes with confidence and efficiency.
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Cohere Embed
Cohere's Embed emerges as a leading multimodal embedding solution that adeptly transforms text, images, or a combination of the two into superior vector representations. These vector embeddings are designed for a multitude of uses, including semantic search, retrieval-augmented generation, classification, clustering, and autonomous AI applications. The latest iteration, embed-v4.0, enhances functionality by enabling the processing of mixed-modality inputs, allowing users to generate a cohesive embedding that incorporates both text and images. It includes Matryoshka embeddings that can be customized in dimensions of 256, 512, 1024, or 1536, giving users the ability to fine-tune performance in relation to resource consumption. With a context length that supports up to 128,000 tokens, embed-v4.0 is particularly effective at managing large documents and complex data formats. Additionally, it accommodates various compressed embedding types such as float, int8, uint8, binary, and ubinary, which aid in efficient storage solutions and quick retrieval in vector databases. Its multilingual support spans over 100 languages, making it an incredibly versatile tool for global applications. As a result, users can utilize this platform to efficiently manage a wide array of datasets, all while upholding high performance standards. This versatility ensures that it remains relevant in a rapidly evolving technological landscape.
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