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QlooQloo, known as the "Cultural AI," excels in interpreting and predicting global consumer preferences. This privacy-centric API offers insights into worldwide consumer trends, boasting a catalog of hundreds of millions of cultural entities. By leveraging a profound understanding of consumer behavior, our API delivers personalized insights and contextualized recommendations. We tap into a diverse dataset encompassing over 575 million individuals, locations, and objects. Our innovative technology enables users to look beyond mere trends, uncovering the intricate connections that shape individual tastes in their cultural environments. The extensive library includes a wide array of entities, such as brands, music, film, fashion, and notable figures. Results are generated in mere milliseconds and can be adjusted based on factors like regional influences and current popularity. This service is ideal for companies aiming to elevate their customer experience with superior data. Additionally, our premier recommendation API tailors results by analyzing demographics, preferences, cultural entities, geolocation, and relevant metadata to ensure accuracy and relevance.
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Vehicle Acquisition Network (VAN)Vehicle Acquisition Network (VAN) is buy center software for franchise and independent auto dealerships, built for buying used cars directly from private sellers. Dealers use it to buy from consumers in their market before those cars reach the auction lane. How it works: - VAN scans 25+ online marketplaces for for-sale-by-owner vehicles and puts them in one dashboard, matched to your buy box (year, make, model, mileage) inside your territory. - It filters out listings from other dealers, scam listings, and Do-Not-Call numbers, decodes each VIN, and adds live market valuation. - Your buyer calls, texts, and emails sellers from inside VAN, and every conversation stays attached to the vehicle. - One click pushes a car into vAuto, where your manager appraises it. CARFAX and AutoCheck reports run inside VAN. - VAN runs inside VINCUE Buy Center Pro as VAN Mode, where VINCUE applies your buy plan to each private-seller car. Plans: - Professional ($1,695/month): the core platform plus an Acquisition Coachâ„¢, an experienced private-party buyer who meets with your team. Every plan includes one. - VAN Accelerateâ„¢ ($2,495/month): adds automated text and email outreach, automated follow-up for unresponsive sellers, automatic replies to KBB ICO leads within minutes, and automatic responses to AccuTrade leads. - VAN Managed Buyerâ„¢ ($5,995/month, 90-day initial term): VAN hires, trains, and manages a full-time buyer who works only for your store on sourcing, outreach, and negotiation. Your desk inspects, makes the final buy decision, and signs the check. Listed prices are starting rates; final price depends on territory size and number of users. Software plans go live in about four days, and Canadian dealers are billed in CAD. VAN was founded in 2013 by Tom Gregg and serves dealers in the United States, Puerto Rico, and Canada. To see VAN on your market, call 855-952-4949.
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Google Cloud BigQueryBigQuery serves as a serverless, multicloud data warehouse that simplifies the handling of diverse data types, allowing businesses to quickly extract significant insights. As an integral part of Google’s data cloud, it facilitates seamless data integration, cost-effective and secure scaling of analytics capabilities, and features built-in business intelligence for disseminating comprehensive data insights. With an easy-to-use SQL interface, it also supports the training and deployment of machine learning models, promoting data-driven decision-making throughout organizations. Its strong performance capabilities ensure that enterprises can manage escalating data volumes with ease, adapting to the demands of expanding businesses. Furthermore, Gemini within BigQuery introduces AI-driven tools that bolster collaboration and enhance productivity, offering features like code recommendations, visual data preparation, and smart suggestions designed to boost efficiency and reduce expenses. The platform provides a unified environment that includes SQL, a notebook, and a natural language-based canvas interface, making it accessible to data professionals across various skill sets. This integrated workspace not only streamlines the entire analytics process but also empowers teams to accelerate their workflows and improve overall effectiveness. Consequently, organizations can leverage these advanced tools to stay competitive in an ever-evolving data landscape.
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FinOpslyAsk a CFO what the company spent on AI last quarter and you will get a number. Ask which product line it belonged to, whether anyone approved it, or what it earned, and the room goes quiet. FinOpsly was built for that second set of questions. It is an AI Cost Governance platform. AI does not run in isolation, so FinOpsly does not price it in isolation either. A model call pulls warehouse queries, GPU time and storage behind it, and the engineers building the feature are burning licensed seats the whole time. All of that lands in one cost model, mapped to the company's own structure: owner, team, product, business unit, customer. What teams use it for: Pricing a workload before anyone provisions anything. Describe the architecture, get a cost estimate across the stack, and see which assumptions drove it. Compare model options using consumption you have already paid for. Making chargeback something finance trusts. Hierarchies run nine levels or deeper. Tags get standardized across providers that never agreed on a convention. API keys and resources are labeled in bulk from instructions written in ordinary English. Anything still unowned shows up as a dollar figure. Holding the line during the month. Budgets by team, project or key. Anomalies flagged with a root cause and sent to the person responsible. Waste that provider consoles do not catch, found by FinOpsly's own detection models. Idle compute parked on schedules the customer approved, and reversible. Proving the outcome. One chargeback run covering AI, cloud, data and SaaS together. Savings measured against the base-line along with cost-to-serve metrics: cost per active user, per customer served. Customers have moved attributable spend from 68% to 99% inside 90 days and taken a chargeback cycle from 12.4 days down to under one. Built for CIOs, CTOs, FinOps practitioners and the finance teams who sign off on the bill.
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Google Cloud Speech-to-TextAn API driven by Google's AI capabilities enables precise transformation of spoken language into written text. This technology enhances your content with accurate captions, improves the user experience through voice-activated features, and provides valuable analysis of customer interactions that can lead to better service. Utilizing cutting-edge algorithms from Google's deep learning neural networks, this automatic speech recognition (ASR) system stands out as one of the most sophisticated available. The Speech-to-Text service supports a variety of applications, allowing for the creation, management, and customization of tailored resources. You have the flexibility to implement speech recognition solutions wherever needed, whether in the cloud via the API or on-premises with Speech-to-Text O-Prem. Additionally, it offers the ability to customize the recognition process to accommodate industry-specific jargon or uncommon vocabulary. The system also automates the conversion of spoken figures into addresses, years, and currencies. With an intuitive user interface, experimenting with your speech audio becomes a seamless process, opening up new possibilities for innovation and efficiency. This robust tool invites users to explore its capabilities and integrate them into their projects with ease.
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LTXLTX builds open world models, AI systems that generate, simulate, and shape video, audio, and the physical world. Lightricks created LTX so that developers, studios, and enterprises can own the model they build on, not just rent access to someone else's. The current release, LTX-2.5, is a 22B-parameter dual-stream diffusion transformer. It renders native 4K footage at up to 50fps and produces synchronized audio and video in one pass, no separate tools required. Independent benchmarks from Artificial Analysis place LTX in the top three AI video models worldwide. There is no single way to work with LTX. Pull the open weights and run the model yourself on your own machines. Take a commercial license for on-premise deployment with full enterprise support. Or use LTX Studio, the packaged production suite for creative teams that want the model without managing the infrastructure. ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA all build on it today. If you need a quick clip for social media, look elsewhere. LTX exists for AI teams turning video, audio, and simulation into part of their own product, not a novelty.
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EBizChargeEBizCharge stands out as a premier provider of integrated payment solutions, enabling businesses to streamline electronic payment processing, bolster transaction security, and boost their profit margins. By equipping companies with the essential tools for faster, safer, and more cost-effective transactions, EBizCharge delivers a top-tier payment processing experience. Their applications adhere to PCI compliance and seamlessly integrate with leading ERP and accounting systems such as QuickBooks, various Sage ERP products, SAP Business One, Microsoft Dynamics, NetSuite, Epicor, and Acumatica, alongside major online shopping platforms like Magento, WooCommerce, and Volusion. This comprehensive integration ensures that businesses can operate efficiently while maintaining high standards of security and convenience.
What is Mu?
On June 23, 2025, Microsoft introduced Mu, a cutting-edge language model boasting 330 million parameters and designed to significantly improve the agent experience in Windows environments by seamlessly converting natural language questions into functional calls for Settings, with all operations executed on-device via NPUs at an impressive speed exceeding 100 tokens per second while maintaining high accuracy. Utilizing Phi Silica optimizations, Mu's encoder-decoder architecture employs a fixed-length latent representation that notably minimizes computational requirements and memory consumption, achieving a 47 percent decrease in first-token latency and delivering a decoding speed that is 4.7 times faster on Qualcomm Hexagon NPUs in comparison to traditional decoder-only models. Furthermore, the model is enhanced by hardware-aware tuning methodologies, which incorporate a strategic 2/3–1/3 division of encoder and decoder parameters, shared weights for both input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, facilitating rapid inference rates that surpass 200 tokens per second on devices like the Surface Laptop 7, along with response times for settings-related queries that are under 500 ms. This impressive blend of features and optimizations establishes Mu as a revolutionary development in the realm of on-device language processing capabilities, setting new standards for speed and efficiency. As a result, users can expect a more intuitive and responsive experience when interacting with their Windows settings through natural language.
What is Gemma 2?
The Gemma family is composed of advanced and lightweight models that are built upon the same groundbreaking research and technology as the Gemini line. These state-of-the-art models come with powerful security features that foster responsible and trustworthy AI usage, a result of meticulously selected data sets and comprehensive refinements. Remarkably, the Gemma models perform exceptionally well in their varied sizes—2B, 7B, 9B, and 27B—frequently surpassing the capabilities of some larger open models. With the launch of Keras 3.0, users benefit from seamless integration with JAX, TensorFlow, and PyTorch, allowing for adaptable framework choices tailored to specific tasks. Optimized for peak performance and exceptional efficiency, Gemma 2 in particular is designed for swift inference on a wide range of hardware platforms. Moreover, the Gemma family encompasses a variety of models tailored to meet different use cases, ensuring effective adaptation to user needs. These lightweight language models are equipped with a decoder and have undergone training on a broad spectrum of textual data, programming code, and mathematical concepts, which significantly boosts their versatility and utility across numerous applications. This diverse approach not only enhances their performance but also positions them as a valuable resource for developers and researchers alike.
Integrations Supported
AiAssistWorks
C++
F#
Gemma
Google AI Studio
HTML
JavaScript
Kaggle
Kotlin
LangChain
API Availability
API Availability
Has API
Pricing Information
Pricing not provided
Pricing Information
Pricing not provided
Supported Platforms
Windows
Supported Platforms
Windows
Mac
On-Prem
Linux
Customer Service / Support
Standard Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Company Facts
Organization Name
Microsoft
Date Founded
1975
Company Location
United States
Company Website
blogs.windows.com/windowsexperience/2025/06/23/introducing-mu-language-model-and-how-it-enabled-the-agent-in-windows-settings/
Company Facts
Organization Name
Company Location
United States
Company Website
ai.google.dev/gemma
Categories and Features
AI Models
Not specified
Small Language Models
Not specified