LM-Kit.NET is a complete local AI runtime for .NET that lets engineering teams ship AI-powered features without cloud dependencies, per-token costs, or data leaving the network.
Most .NET AI integrations stop at inference. LM-Kit.NET covers the full range of capabilities production applications actually need: agentic workflows with tool calling, planning, and memory; document intelligence with OCR and structured extraction; retrieval-augmented generation with built-in vector storage; multilingual speech-to-text; vision and multimodal understanding; text analysis with classification, NER, PII extraction, and sentiment; and text generation with translation, summarization, and constrained output.
Ships in one NuGet package, runs in-process with no sidecar services, and works across all major hardware acceleration backends. Drop-in replacement for Semantic Kernel through its Microsoft.Extensions.AI compatibility layer.
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ARGOS Identity makes two products: ARGOS ID Check for identity verification (KYC/eKYC) and ARGOS Omni, a Review Ops Agent that reviews documents and data case by case.
ARGOS ID Check combines ID document recognition and authenticity checks (OCR, MRZ and driver's license PDF417 barcode checks, ID liveness against photocopies and screen replays), selfie-to-ID face comparison, passive and active liveness that detects photos and deepfakes, and AML screening against sanctions and PEP lists with ongoing monitoring. It covers 4,000+ ID document types, including passports, national IDs, driver's licenses and residence permits, from 200+ countries. Teams ship it as a hosted Liveform (one URL, 26 languages, web and app WebViews) or build their own UI on the Verify engine APIs, and receive results by webhook, return URL or REST API. ARGOS Score thresholds and custom policy rules decide each case, and pending cases go to your team or ARGOS Expert Reviewer. ID Check is ISO/IEC 27001 certified.
ARGOS Omni is a Review Ops Agent for document and data review. Teams write their review criteria in plain language; Omni turns them into a playbook and, for each case, checks the submitted documents, extracts data into the output schema you define, cross-checks documents against each other and runs external lookups such as AML screening of people and companies. It returns verified, rejected or pending_review, and records the reason and source step behind every value. Use cases include KYB document review for business onboarding, vendor onboarding, corporate telecom subscription review, insurance claim review and AML review.
Pricing is usage-based and quoted by sales.
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ScholarIQ
ScholarIQ is a web-based AI research assistant for searching and reviewing scientific literature. Researchers can pose a question, search records from OpenAlex, ORCID and PubMed, or upload search results and source documents. The platform ranks articles against review criteria, supports citation-based snowball discovery, and helps users screen records, highlight relevant passages, and extract specified information from uploaded texts. Answers and suggested values link to cited sources, while reviewers retain control of inclusion decisions, risk-of-bias judgments, and conclusions. ScholarIQ records reviewer identity, decision rationale, and supporting passages to make review work traceable. It also provides a public research metadata atlas linking papers, researchers, journals, institutions, and topics. It serves individual researchers and review teams doing literature searches, evidence screening, and systematic reviews.
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