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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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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Qdrant
Qdrant serves as a sophisticated vector similarity engine and database, functioning as an API service that enables the search for the closest high-dimensional vectors. By utilizing Qdrant, users can transform embeddings or neural network encoders into comprehensive applications designed for matching, searching, recommending, and far more. It also offers an OpenAPI v3 specification, which facilitates the generation of client libraries in virtually any programming language, along with pre-built clients for Python and other languages that come with enhanced features. One of its standout features is a distinct custom adaptation of the HNSW algorithm used for Approximate Nearest Neighbor Search, which allows for lightning-fast searches while enabling the application of search filters without diminishing the quality of the results. Furthermore, Qdrant supports additional payload data tied to vectors, enabling not only the storage of this payload but also the ability to filter search outcomes based on the values contained within that payload. This capability enhances the overall versatility of search operations, making it an invaluable tool for developers and data scientists alike.
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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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