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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For developers, ARGOS Identity offers two APIs.
ARGOS ID Check (KYC/eKYC): launch the hosted Liveform from one URL in a browser or an iOS, Android or React Native WebView, or build your own capture UI on the Verify engine APIs. Your backend uses the REST API to fetch submissions, review pending cases, delete or partially delete data for GDPR and CCPA requests and download AML reports, and receives webhooks for created, submit, approved, rejected, updated, delete and AML result events. Each check reads the ID with OCR plus MRZ and PDF417 barcode checks, tests ID and selfie liveness, matches the face to the ID photo and screens the user against sanctions and PEP lists, with ongoing monitoring afterwards. Data is processed and stored in the region chosen for each project (US or South Korea), sensitive fields are encrypted with AES-256 at rest, and payload encryption is optional. A migration API imports existing verification records.
ARGOS Omni (Review Ops Agent): authenticate with an x-api-key header, create a profile under a workflow, upload files, text or JSON as items (PDF, DOCX, XLSX, PPTX, images, CSV, HTML, EML and more) and start an asynchronous analysis. The result follows the output schema you defined, with a verdict of verified, rejected or pending_review and the reasoning and source step for each extracted value, and every completed analysis can be downloaded as a PDF report.
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Agent Bayes
Agent Bayes serves as an AI-driven research aide tailored for graduate students, postdocs, and academics alike. Users can upload papers in PDF format or sync their libraries from Zotero, subsequently posing inquiries related to their collection. The assistant meticulously searches through these materials, constructing a mindmap that illustrates claims, each connected to the specific passage and page that substantiates it. Users have the ability to click on a citation to view the relevant passage or PDF page, allowing competing research findings to coexist on the map as distinct sourced viewpoints. Additionally, the AI Verification feature evaluates whether the cited passages adequately support a claim, although this capability is reserved for those on paid plans. An MCP server facilitates the integration of compatible tools like Claude Code, Codex, and Cursor, enabling them to search the user's library and leverage its cited answers and mindmaps. The free plan offers 50 credits each month, while paid subscriptions initiate at $11 per month, billed on an annual basis, providing users with a range of options to enhance their research experience. This innovative assistant aims to streamline the research process and improve academic productivity.
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ScholarIQ
ScholarIQ is an online AI-powered research assistant designed to facilitate the search and analysis of scientific literature. It allows researchers to ask questions, access records from databases such as OpenAlex, ORCID, and PubMed, or even upload their own search results and source documents. The platform evaluates articles based on specific review criteria, enhances citation-based discovery through snowballing techniques, and aids users in efficiently screening records, highlighting pertinent excerpts, and extracting desired information from uploaded materials. Furthermore, it provides answers and suggested values that are linked to their respective citations, ensuring that reviewers maintain authority over decisions regarding inclusion, risk-of-bias assessments, and overall conclusions. ScholarIQ meticulously documents reviewer identities, rationale for decisions, and relevant excerpts to ensure that the review process is transparent and accountable. Additionally, it features a public research metadata atlas that connects various papers, researchers, journals, institutions, and topics, thereby fostering a more interconnected research environment. This platform is invaluable for both individual researchers and review teams engaged in thorough literature searches, evidence screening, and the execution of systematic reviews, making the research process more efficient and reliable.
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