LM-Kit.NET
LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents.
Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development.
Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.
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optivalue.ai
Questionnaires, audits, and RFPs consume a significant amount of your experts' time. Turn this administrative burden into an engine to win.
Optivalue.ai reduces response times by up to 90% by automating information discovery and response drafting, allowing your experts to focus on the high-impact personalization that wins bids.
Here's how it works:
Understanding: Connected to your systems, it acts as an expert librarian. It reads and understands your entire knowledge base to know precisely where the best information is for any question.
Submission: You submit a questionnaire to it.
Response: In minutes, it generates a complete draft response using the most relevant excerpts from your own documents.
Every answer becomes a verified fact. For perfect traceability, every statement is substantiated. Optivalue.ai precisely cites the source document, page, and date. You don't just answer correctly—you prove it.
It’s an engine for organizational improvement. Optivalue.ai performs a gap analysis to identify weaknesses in your documentation. The proposed improvements build your team's expertise. By implementing these recommendations to update your internal documents, you drive lasting progress across your entire organization.
Your data security is guaranteed. Optivalue.ai is built with enterprise-grade security, compliant with strict standards like GDPR, HIPAA, ISO, and FedRAMP, allowing you to manage your most sensitive data with complete confidence.
All our plans include unlimited users and projects.
Start your 14-day free trial.
No credit card required. No commitment.
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Resea.AI
Resea AI serves as a comprehensive academic research assistant, adept at independently planning, executing, and composing extensive academic projects, ranging from literature reviews to the drafting of reports. This innovative tool integrates effortlessly with key scholarly databases including Google Scholar, PubMed, and arXiv to gather reliable research, utilizing its unique "Think and Research" engine to navigate the research process, identify key themes, and explore various writing perspectives through a multi-tiered inquiry approach. Its advanced AI writing editor can produce documents of virtually any length, reaching up to 50,000 words, and provides interactive editing features for swift adjustments. To uphold academic integrity, Resea AI supports numerous citation formats and ensures precise source indexing. Moreover, it assesses its effectiveness through benchmarks like xBench‑DeepSearch, which gauges its deep research capabilities. The platform also accommodates a variety of applications, such as systematic literature reviews, the creation of academic outlines, content synthesis, and feedback from a reviewer’s perspective, making it an invaluable resource for researchers and students alike. As a result, Resea AI not only streamlines the research process but also enhances the overall quality of academic writing.
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Connected Papers
Connected Papers is an innovative visual instrument aimed at aiding researchers and applied scientists in uncovering and navigating academic literature relevant to their specific areas of study. By submitting a "seed paper," users can create a visual graph that illustrates related papers, utilizing a similarity metric based on analyses of co-citation and bibliographic coupling. This method enables users to discover significant literature, even in cases where direct citations may not exist. The generated graph offers a clear visual representation of the research ecosystem, emphasizing key works and highlighting possible directions for further investigation. By enhancing the literature review process, Connected Papers strives to make it more efficient and thorough for researchers, ultimately fostering a deeper understanding of their fields. Moreover, this tool encourages a more interconnected view of research by revealing unexpected relationships between studies.
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