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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Gaffa is a REST API built for web scraping and browser automation, allowing developers to run real, full browsers at scale with a single API call. It removes the difficulty of managing headless browser frameworks, rotating proxies, CAPTCHA solving, and scaling infrastructure, all of which are handled automatically.
JavaScript-heavy and dynamic websites render exactly as they would for a human visitor by default. Beyond standard scraping, Gaffa supports AI-driven structured data extraction (extract data into a defined schema without writing CSS selectors), screenshot and PDF capture, infinite-scroll and form-filling automation, and clean Markdown conversion for feeding webpages directly into LLM and RAG pipelines.
A rotating residential proxy network keeps access reliable across regions, and a credit-based pricing model means teams pay only for the browser time and bandwidth they actually use. Gaffa is designed for AI engineers, data teams, and developers who want production-grade web data extraction without having to build and maintain their own infrastructure.
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PDF.co
An API platform designed for intelligent extraction of data from PDFs facilitates automated parsing of documents. Users can create reusable low-code templates for data extraction, supporting multiple languages for OCR as well as tables and fields. The platform features a built-in invoice parser along with capabilities to split, merge, reorder, and delete pages in PDF files. Advanced splitting tools are available, allowing for the filling out of PDF forms and the addition of text, images, and signatures to existing documents. It also includes auto-filling for interactive fields and the ability to generate PDFs from HTML templates while allowing for conditions, variables, and custom logic. Users enjoy high-quality PDF output with full control over quality, ensuring secure and scalable operations. The PDF extractor engine converts documents into formats such as raw JSON, CSV, XML, XLS, and XLSX while preserving layout and efficiently extracting tables. Additionally, the platform offers OCR capabilities to repair malformed text and extract various barcode types, including QR Codes, Code 128, Code 39, DataMatrix, and PDF417 from PDFs, scans, and images, all supported by a high-performance barcode reading engine. With such robust features, this platform stands out as a comprehensive solution for all PDF-related data extraction needs.
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Unsiloed
Unsiloed AI is an enterprise document intelligence platform built to transform unstructured documents into structured, LLM-ready data. The platform processes PDFs, images, spreadsheets, scans, and multimodal files, then outputs clean JSON, Markdown, or structured fields for AI agents, LLM applications, vector databases, and data warehouses. Its core capabilities include parsing, extraction, and document splitting, allowing teams to use each function independently or chain them into a full production pipeline. Unsiloed’s parser converts complex documents into Markdown while preserving structure across text, tables, charts, figures, forms, handwriting, signatures, and visual hierarchy. Its extraction engine pulls schema-specific fields into JSON and uses domain awareness to understand documents such as invoices, contracts, financial reports, healthcare records, and regulatory filings. Its splitting tools can separate mixed files into individual documents or break long documents into retrievable chunks while preserving parent-child relationships and surrounding context. The platform is powered by proprietary dual-stream vision models that combine a data stream for tokens and entities with a layout stream for bounding boxes, alignment, indentation, and visual structure. Unsiloed is designed to solve the problem of fragile OCR and DIY pipelines that break when document layouts change. For enterprise AI teams, Unsiloed provides a more reliable document layer for turning high-value unstructured data into assets that can be searched, reasoned over, and used in production AI systems.
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