EBizCharge
EBizCharge is the leading embedded payments application for businesses to accept payments directly inside QuickBooks, Microsoft Dynamics, NetSuite, SAP, Acumatica, and 100+ other business systems. Trusted by 20,000 companies, EBizCharge combines modern billing tools with integrated payment processing to help B2B companies get invoices paid faster, eliminate manual work, and keep payment data automatically synced to their ERP. Companies use EBizCharge to:
◉ Accept credit card, debit card, and ACH payments natively inside ERP, CRM, or eCommerce platforms
◉ Speed up collections with easy billing tools: payment links, online customer portal, recurring billing, saved cards, and more
◉ Improve security and reduce risk with PCI-compliance, encryption, tokenization, fraud protection, and certified by the PCI-Security Council
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HOW IT WORKS IN YOUR ERP, CRM, & E-COMMERCE PLATFORMS
EBizCharge integrates natively with your ERP, CRM, or e-commerce platform through certified software connections, so payments work directly inside the system you already use.
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FEATURES
• Email payment links
• Recurring billing
• Secure online customer payment portal
• Securely save cards
• EMV terminals
• Mobile payments
• Ability to surcharge
• Dedicated in-house support
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Couchbase
Couchbase’s operational data platform for AI is a scalable foundation for enterprise operational, analytical, mobile and AI workloads that replaces legacy infrastructure and data services. Couchbase connects and mobilizes your data, so you can power peak experiences, harness the power of AI and scale globally—all with less risk and lower overhead.
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Superlinked
Integrate semantic relevance alongside user feedback to effectively extract the best document segments in your retrieval-augmented generation framework. Additionally, merge semantic relevance with document recency in your search engine, as newer content is often more precise. Create a dynamic, personalized e-commerce product feed that utilizes user vectors derived from SKU embeddings that the user has engaged with. Analyze and identify behavioral clusters among your customers through a vector index housed in your data warehouse. Methodically outline and load your data, utilize spaces to build your indices, and execute queries—all within the confines of a Python notebook, ensuring that the entire process remains in-memory for efficiency and speed. This approach not only optimizes data retrieval but also enhances the overall user experience through tailored recommendations.
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Asimov
Asimov serves as a fundamental platform for AI-search and vector-search, allowing developers to upload various content sources such as documents and logs, which it then automatically chunks and embeds, making them accessible through a single API for enhanced semantic search, filtering, and relevance for AI applications. By streamlining the management of vector databases, embedding pipelines, and re-ranking systems, it simplifies the process of ingestion, metadata parameterization, usage monitoring, and retrieval within a cohesive framework. With features that support content addition through a REST API and the capability to conduct semantic searches with tailored filtering options, Asimov empowers teams to create extensive search functionalities with minimal infrastructure requirements. The platform efficiently manages metadata, automates chunking, handles embedding, and facilitates storage solutions like MongoDB, while also offering user-friendly tools such as a dashboard, usage analytics, and smooth integration capabilities. Furthermore, its all-in-one approach eliminates the complexities of traditional search systems, making it an indispensable tool for developers aiming to enhance their applications with advanced search capabilities.
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