Organizations are eager to move into the era of agentic AI, but moving AI projects from development to production remains a challenge. Deploying agentic AI apps often requires complex configurations and integrations, delaying time to value.
Barriers to deploying agentic AI:
Now, DataRobot comes with NVIDIA AI Enterprise embedded — offering the fastest way to develop and deliver agentic AI.
With a fully validated AI stack, organizations can reduce the risks of open-source tools and DIY AI while deploying where it makes sense, without added complexity.
This enables AI solutions to be custom-tailored for business problems and optimized in ways that would otherwise be impossible.
In this blog post, we’ll explore how AI practitioners can rapidly develop agentic AI applications using DataRobot and NVIDIA AI Enterprise, compared to assembling solutions from scratch. We’ll also walk through how to build an AI-powered dashboard that enables real-time decision-making for warehouse managers.
Imagine that you’re a warehouse manager trying to decide whether to hold shipments upstream. If the warehouse is full, you need to reorganize your inventory efficiently. If it’s empty, you don’t want to waste resources; your team has other priorities
But manually tracking warehouse capacity is time-consuming, and a simple API won’t cut it. You need an intuitive solution that fits into your workflow without required coding.
Rather than piecing together an AI app manually, AI teams can rapidly develop a solution using DataRobot and NVIDIA AI Enterprise. Here’s how:
This is just one example of how AI teams can build agentic AI apps faster with DataRobot and NVIDIA.
Building agentic AI applications is an iterative process that requires balancing integration, performance, and adaptability. Success depends on seamlessly connecting — LLMs, retrieval systems, tools, and hardware — while ensuring they work together efficiently.
However, the complexity of agentic AI can lead to prolonged debugging, optimization cycles, and deployment delays.
The challenge is delivering AI projects at scale without getting stuck in endless iteration.
Flexible starting points with NVIDIA AI Blueprints and DataRobot AI Apps
Choose between NVIDIA AI Blueprints or DataRobot AI Apps to jumpstart AI application development. These pre-built reference architectures lower the entry barrier by providing a structured framework to build from, significantly reducing setup time.
To integrate NVIDIA AI Blueprint for video search and summarization, simply import the blueprint from the NVIDIA NGC gallery into your DataRobot environment, eliminating the need for manual setup.
Accelerating predictive AI with RAPIDS and DataRobot
To build the forecast, teams can leverage RAPIDS data science libraries along with DataRobot’s full suite of predictive AI capabilities to automate key steps in model training, testing, and comparison.
This enables teams to efficiently identify the highest-performing model for their specific use case.
Optimizing RAG workflows with NVIDIA NIM and DataRobot’s LLM Playground
Using the LLM playground in DataRobot, teams can enhance RAG workflows by testing different models like the NVIDIA NeMo Retriever text reranking NIM or the NVIDIA NeMo Retriever text embedding NIM, and then compare different configurations side by side. This evaluation can be done using an NVIDIA LLM NIM as a judge, and if desired, augment the evaluations with human input.
This approach helps teams identify the optimal combination of prompting, embedding, and other strategies to find the best-performing configuration for the specific use case, business context, and end-user preferences.
Deploying AI isn’t the finish line — it’s just the start. Once live, agentic AI must adapt to real-world inputs while staying consistent. Continuous monitoring helps catch drift, bugs, and slowdowns, making strong observability tools essential. Scaling adds complexity, requiring efficient infrastructure and optimized inference.
AI teams can quickly become overwhelmed with balancing development of new solutions and simply keeping existing ones.
For our agentic AI app, DataRobot and NVIDIA simplify management while ensuring high performance and security:
Managing agentic AI applications over time requires maintaining compliance, performance, and efficiency without constant intervention.
Continuous monitoring helps detect drift, regulatory risks, and performance drops, while automated evaluations ensure reliability. Scalable infrastructure and optimized pipelines reduce downtime, enabling seamless updates and fine-tuning without disrupting operations.
The goal is to balance adaptability with stability, ensuring the AI remains effective while minimizing manual oversight.
DataRobot, accelerated by NVIDIA AI Enterprise, delivers hyperscaler-grade ease of use without vendor lock-in across diverse environments, including self-managed on-premises, DataRobot-managed cloud, and even hybrid deployments.
With this seamless integration, any deployed models get the same consistent support and services regardless of your deployment choice — eliminating the need to manually set up, tune, or manage AI infrastructure.
DataRobot with NVIDIA embedded accelerates development and deployment of AI apps and agents through simplifying the process at the model, app, and enterprise level. This enables AI teams to rapidly develop and deliver agentic AI apps that solve complex, multistep use cases and transform how end users work with AI.
To learn more, request a custom demo of DataRobot with NVIDIA.
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Artificial Intelligence technology has touched almost every aspect of life and the environment. In particular, Speech/Voice recognition is one of the trending technology belonging to AI that made human-to-machine interactions simple and faster.
Voice recognition or speech recognition is gaining popularity around the world. The rise of AI and voice-controlled intelligent assistants have made virtual interactions over a secured platform easier and more convenient. These cutting-edge digital technologies can convert human speech into machine-understandable code and revert back to human spoken language. Google Assistant, Microsoft Cortana, Apple’s Siri, and Amazon’s Alexa are a few best examples of speech recognition apps that have attained huge popularity in the market.
According to market research reports, 74% of smartphone users or digital customers rely on voice-based assistants to search for products and services, open their favorite playlists, make a shopping list, track the mailbox, etc. Such wide acceptance of AI and speech recognition applications is a profitable note for voice recognition technology companies.
It is expected that the global voice recognition apps market is expected to reach $18 billion by the year 2023, at a compound annual growth rate of 23.89%. So, businesses can either integrate AI features in existing apps or start to invest in AI development to grab market opportunities.
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Whether it’s on a standalone home device or Smartphone, the benefits delivered by digital voice assistants are incredible. Therefore, speech recognition AI will offer unbelievable businesses growth opportunities for organizations. The opportunities of developing AI-powered voice-controlled apps or speech recognition applications has great scope in the future.
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Google Assistant is one of the best voice recognition applications. It is a popular speech recognition application developed using AI. This Artificial Intelligence (AI) enabled and voice-powered virtual assistant software. It is known as one of the trending and advanced virtual assistants.
Google has partnered with a number of firms to make Google Assistant available on large number of smart devices such as head phones, smart phones, cars, fridges, etc.
Here, you can learn about what is Google Assistant and how does it work?
Google speech recognition app supports both text and voice entry and uses the latest AI technology like NLP (natural language processing). It provides a variety of services, including voice search, voice commands, assists tasks, translates in real-time, sends reminders, finds information online, makes appointment, and many more.
Google speech recognition app has also expanded its virtual assistant services significantly. It supports 10,000 devices across over 1000 brands.
No doubt. Alexa is one of the best voice recognition apps for android and is also recognized as one of the top voice recognition apps for iPhone. Alexa is also known as Amazon Alexa, is an AI-based virtual assistant from the multinational company, Amazon. Initially, it was tried with Amazon Echo dot smart speakers and Amazon Echo. Anyway, currently, it is not available on iOS and Android platforms.
Amazon Alexa uses NLP, voice queries, etc. to provide services like music playback and voice interaction. In addition, it will set up alarms, creates to-do lists, plays audiobooks, and streams podcasts. Moreover, Alexa can also give live information about traffic, the latest news and sports, and the weather which are very useful.
You can experience this virtual assistant on Mac and Windows, and it requires some easy steps to setup. Some PCs accept Alexa’s hands-free activation, while others need you to click the Alexa icon.
Can AI do voice recognition? The best answer is Siri-like famous voice recognition applications. Similar to Alexa and Google speech recognition app, Siri is one of the best AI apps for speech recognition.
Siri is one of the Top 5 AI apps for speech recognition applications which is delivering top-notch performance and assisting iPhone users virtually in many ways. It is a well-known virtual assistant recognized speech software from the Apple Company.
Siri uses Voice Questions and the Natural Language User Interface (UI) to work and send text messages, make calls, answer queries and make recommendations. It assigns requests to many Internet services. Besides, Siri adapts to users’ searches, language, and preferences.
The most popular voice recognition apps for android and iOS. Another strong AI application that does not require an introduction is Cortana, a virtual assistant from the tech giant, Microsoft. Cortana provides problem-solving, hands-free assistance, answers queries, performs specific tasks, takes notes, gives reminders, and assists in calendar management. Over time, Microsoft’s Cortana “learns” and performs complex tasks.
Cortana uses information from Bing search engines, NLP, and other devices to provide personalized recommendations, and it consists of a speech API that greatly works with Windows and 3rd party applications.
This voice recognition software is available on iOS, Android, Windows Mobile, Microsoft Band, Windows 10, Windows Mixed Reality (MR), Invoke Smart Speaker, and Xbox One.
You can also see Cortana on famous headsets such as the CloudX, HyperX, Logitech G933, Sennheiser GSP350, and Razor Kraken 7.1 V2.
It is one of the top 5 AI Apps for speech recognition. The Hound app, Silicon Valley Company named ‘SoundHound’ has launched this Hound application. This virtual assistant app answers the queries of users and completes several tasks such as calculations and recognizing words correctly in the process with 95% accuracy.
The Hound app can send a text message and make calls for you. You can also have information about weather conditions, breaking news, hotels, and restaurants nears by, call Ola or Uber, check the status of the flight, etc. This digital assists app is available on both iOS and Android.
This best AI voice recognition software app uses its Deep Meaning Understanding and Speech-to-Meaning technologies. Hound has an excellent range of customers, e.g., Honda, Mercedes Benz, Hyundai, and Motorola.
These are the top 5 AI apps developed using speech recognition technology. All apps have been crowned as the best AI apps in 2022. Speech recognition in AI is really a gift for voice recognition technology companies to create miracles with technology. Voice recognition technology companies are assisting businesses across different verticals in creating futuristic speech recognition AI software applications.
In 2025, there are various AI speech recognition tools that can accurately and rapidly convert speech into text. Some of the well-known ones are:
An extremely accurate tool that can transcribe speech in real-time and has support for various languages.
A strong speech recognition functionality that includes transcription as well as translation. It will be utilized to develop most of the voice-enabling applications.
A cloud service for speech-to-text transcription, ideal for customer call, meeting, or media transcription with multi-accents and language support.
A speech recognition platform powered by AI that prioritizes high accuracy for business use, with features such as real-time transcriptions and training custom models.
Highly accurate transcription is what Rev AI is best known for, and it is commonly used to transcribe podcasts, interviews, and videos.
These AI tools for speech recognition in 2025 simplify the process of integrating speech recognition capabilities into products for developers and businesses, making it easier to improve accessibility, customer service, and user engagement.
The cost of building an AI voice generator app may be $50,000 to $150,000 based on features, platform, and AI complexity. User interface design, voice generation API integration, and voice processing and storage with cloud infrastructure will also impact the overall cost of developing an AI Voice app.
Further, features such as multi-language integration will increase the final cost of developing an AI text-to-speech app. Moreover, licensing AI models or cloud services, such as AWS or Google Cloud, will also be an additional expense to the estimated cost of building an AI voice generator app.
However, additional costs for developing an AI text-to-speech app can be added for fine-tuning the models to achieve high-quality, natural-sounding voices. Maintenance, server prices, and security features also contribute to the overall cost of developing an AI voice app.
To develop voice recognition apps for iPhone or voice recognition apps for Android, AI development companies follow a precise process to ensure the successful app development.
Firstly, to create an AI-driven language learning app, incorporate AI technologies such as natural language processing (NLP) and speech recognition for customized lessons. Machine learning can adjust content to the skill level of the user. Real-time translation, grammar check, and pronunciation feedback are the highlight features that utilize APIs such as Google Translate or IBM Watson.
Second, develop a backend infrastructure for handling user data and tracking advancement. Secure platforms in the cloud, such as AWS or Firebase, make safe data storage possible. Facilitate privacy compliance and provide offline accessibility for lessons. Integration of social capabilities such as native speaker language exchange makes it a better experience. Periodic updates of content and AI updates will make the app efficient and topical.
As per the research reports of Trend force, the voice recognition solutions market across the globe is forecasted to reach USD 15.98 billion by the year 2021. Virtual/ speech recognition is becoming an integral part of AI systems and it gives more communication to users.
Thanks to natural language processing, it is constantly learning from us and making necessary changes in the way we live, and perform. Businesses across the globe are expected to invest more in this speech recognition technology as the whole voice infrastructure improves soon.
Planning to Develop an AI-Powered Speech Recognition App for your Business?
USM Business Systems is providing cutting-edge AI solutions to transform your business. We have a team of experienced and professional AI developers who gained unbelievable hands-on best practices in using various advanced technologies.
Contact us today. We would love to assist you!
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Apple has long been a trailblazer in the tech world, setting trends and raising the bar for innovation. Now, as rumors swirl about the company’s first foldable iPhone, excitement is building among fans and industry watchers alike. With reports suggesting a launch as early as 2026, the foldable iPhone could mark a new chapter for...
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March 17, 2025 — Today marks a pivotal moment for the gaming industry as Discord unveils its groundbreaking Social SDK, a free toolkit designed to seamlessly integrate its robust social features into video games. Announced at the Game Developers Conference (GDC) 2025, this innovative software development kit promises to redefine how players connect, communicate, and...
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Online video streaming applications were ruling the global media services and completely altered the way people explore information. Driven by the increasing availability of high-speed internet coupled with the cumulative use of smartphones, the popularity of video streaming apps is on the rise.
According to Statista, the global video streaming application industry has reported $86 billion in 2021 and it is estimated to reach $115 billion by the next coming three years.
In particular, out of the total market value, smartphone users in the US alone are spending nearly $44 billion on premium audio or video streaming applications during the year. Most of the revenues are generated from the US market. Estimates are also showing that video streaming app development in the US will offer a profitable market to investors.
In this article, after doing deep research on the USA’s video streaming industry, we have compiled a list of top video streaming apps in the USA. Be you invest in Netflix clone app development or Disney+ clone app development, or YouTube clone app development, it will create a valueble addition to your media services.
Let’s look at the top 5 video streaming apps in the USA.
With over 1,000,000,000+ downloads (just from Google Play Store), Netflix has stood as the number #1 online video streaming app in the world. The app is popularizing in providing uninterrupted live video streaming services to users. Users or paid subscribers can watch TV shows and movies on Netflix.
The app is been preferred by nearly 45% of video streaming app users in the USA. Hence, the development of a Netflix-like mobile app for Android or iOS will help you build brand value and reach more audiences faster.
Features and Functionalities Of Top Online Video Streaming Apps like Netflix
With the below features, Netflix stood as the top player in the list of streaming media services. These advanced and user-friendly features also made Netflix one of the best native live streaming apps in 2025.
Key information Of Netflix App – Best Streaming Service for Live TV
YouTube is one of the biggest online video streaming apps with accessibility to billions of customers across 100 countries in the world.
Over 90% of the internet population is downloading and using this global largest video-sharing platform to stream and watch their favourite videos. This is where it has occupied position as the best video streaming apps USA in 2025. The app also facilitates users to subscribe and share video channels instantly.
Top features of YouTube – Video streaming services in USA
Key Information
A video streaming app development like Disney+ will deliver high returns on investment. Disney+ is one of the most popular video streaming apps in the USA. As of records, the app has approximately $150 million subscribers and become one of the top competitors to the Netflix mobile app.
Key features of Disney+ like trending OTT apps in the USA
Key Information
Amazon Prime Video is one of the other American leading video on-demand OTT streaming service apps with nearly 172 million subscribers in the country.
Best features of Amazon Prime best video movie streaming app
The cost to develop video streaming apps for TV like Amazon Prime will depend on these features.
Key Information
The best on-demand video broadcasting applications like Hulu are on our list of top mobile apps for live video streaming. The app has lifted media entertainment to the next level.
Features and functionalities of the Hulu app – Video streaming services in USA
Key Information
For creating the best video streaming apps consider some of the most important steps:
Do you have this question in mind? No worries, we are here.
The estimated cost of video streaming apps will be around $25,000- to $35,000+ for a single platform. The development cost of OTT app development for multiple platforms will be $35,000 to $60,000+ with moderate complexity in design. The live streaming app development cost is based on the complexity you increase.
However, the app development platform, application design, features list, technology stack, app type, and the hourly cost of mobile application development you hire will all impact the cost to build a video streaming app.
Amazon Prime Video, YouTube, and Disney+ like video streaming application development would be the best decision to catch up with the market opportunities. Businesses can generate the flow of revenues by developing paid subscription models.
USM is one of the top video-streaming mobile app development companies in the USA. Our team of mobile app developers is an expert in the design and development of Netflix-like trending OTT apps.
Are you looking to hire a top Video Streaming App Development Company in the USA?
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ChatGPT-Maker’s CEO Sam Altman just disclosed an eye-opening revelation in the Wall Street Journal: Most of the people using ChatGPT are students.
Given that 400 million people now visit the ChatGPT Web site every week, that means approximately 300-350 million of the people using ChatGPT are students (most).
The takeaway: The statistic explains that while ChatGPT can reduce writing time for simple tasks like email by as much as 90% or more, students are the people who have picked-up and run with that realization – not business pros.
That’s a problem for the lion’s share of business people who ‘get’ that AI writing is not simply coming – it’s here – but have yet to add AI to their toolbox.
Essentially: Colleges in the U.S. alone release 4 million new graduates each year into the U.S. workforce.
And you can bet that since 2023 — when ChatGPT became a force to be reckoned with across the globe — most U.S. college graduates walked into their first jobs already knowing how to automate their business writing with AI.
Something tells me their older brothers and sisters have gotten the memo, too.
In other news and analysis on AI writing:
*ChatGPT-Maker Experimenting With Turbo-Charged Creative Writer: OpenAI is currently experimenting with a new AI chatbot it believes produces its best, automated creative writing yet.
Observes OpenAI CEO Sam Altman: “This is the first time I have been really struck by something written by AI.
“It got the vibe of metafiction so right.”
So far, OpenAI has no plans to release the experimental AI writer to the public.
*Google Releases ‘Nearly as Good AI’ That Runs on a Single Chip: Google has released a new family of AI engines that are nearly as good as bleeding edge AI — but are able to run on a single chip.
The theory: By offering somewhat less than state-of-the-art performance, the stripped-down AI will not need a series of expensive chips to produce results.
Instead, the AI can run locally on a desktop, laptop, smartphone or similar hardware – representing major cost savings as compared to going to the cloud for computations.
*New Chinese AI Agent Takes On ChatGPT-Maker: A new, experimental AI agent – dubbed Manus – has gone viral, amazing writers and others with its ability to perform independent, automated tasks on a computer.
The new AI competes directly with a similar AI agent – Operator – which is offered by OpenAI under its $200/month ChatGPT Pro subscription.
Theoretically, writers using the agent-creating software will be able to:
~Automatically research an article on the Web
~Scout for quotes to go along with that research from blogs and press releases
~Auto-write the article in a preferred writing style
~SEO-optimize the article for easy discovery by search engines
~Periodically research the Web for new developments in
the article’s story
~Continually re-rewrite the article as new developments in the article’s story occur
*Local News Media Using AI to Monitor Public Meetings: Nonprofit education news service Chalkbeat is currently using AI to monitor public meetings in about 80 school districts across 30 states.
The tech, dubbed LocalLens, listens-in on the public meetings, then uses AI to transcribe, summarize and archive what’s said.
Observes Eric Gorski, a managing editor at Chalkbeat: “We are going to be in the rooms where we need to be, where the big decisions are being made, but we can’t be everywhere all the time.”
“The summaries are springboards for more reporting. It’s not a replacement for coverage. And we’re not trusting AI to get these things right. It’s more like a news tip.”
Back in the day, when I got my first job in journalism, covering local school district and local government meetings used to be the sole purview of human beings.
And one of those human beings happened to be me.
*Small Business Software Adds AI-Written Reports: Pipedrive, a maker of customer relationship management software, has made it easier for salespeople to auto-generate reports using AI.
Observes Viktoria Ruubel, CPO, Pipedrive: “With AI-powered report creation, sales teams can now shift their focus to what truly matters – analyzing trends, identifying opportunities and making informed decisions within seconds.”
Pipedrive took special care to ensure the reports can be easily generated by natural language prompts.
Plus, the new AI report-writer also comes with 14, prefabricated prompts to generate commonly needed reports.
*AI Fiction Writer Out With an Upgrade: AI creative writing pioneer Sudowrite has released a new update that promises to deliver prose that sounds more original – and eschews clichés.
Dubbed Muse, the new module’s emphasis on originality was engineered in collaboration with hundreds of authors, according to Sudowrite founder James Yu.
Sudowrite currently runs on a number of AI engines, including ChatGPT, Claude and DeepSeek.
*AI-Powered Search Engines Wrong 60% of the Time: AI companies looking to steal Google’s thunder by branching out into search have been hit with a hard dose of reality.
On average, the citations they use to certify their search results to users are wrong more than 60% of the time, according to a new study from the Tow Center – although some tools performed better than others.
Observes writer Andrew Deck: “Perplexity, which brands itself as a tool for research, had the lowest failure rate, answering incorrectly 37% of the time.
“Meanwhile, Grok-3 Search had the highest failure rate at 94%.”
*Microsoft Puts More Daylight Between Itself and ChatGPT-Maker: Microsoft and OpenAI – once the dynamic duo when it came to all things AI – continue to drift apart.
Specifically, Microsoft says its developing in-house AI engines – dubbed MAI — being designed to compete directly with the AI engines that power ChatGPT, Gemini, Claude and Grok.
The news comes on the heels of a report that ChatGPT-maker OpenAI just inked a $11.9 billion dollar deal with CoreWeave, which will provide servers and other infrastructure to help drive OpenAI’s software.
Used to be, Microsoft was the overwhelming, go-to trading partner for such infrastructure services.
*AI BIG PICTURE: AI and Our Future: ChatGPT Competitor Anthropic’s View: This one-hour video from the Council on Foreign Relations offers one of the most lucid perspectives on the anticipated impact of AI in recent memory.
It features an interview with Dario Amodei, CEO, Anthropic – the maker of Claude, a fierce competitor to market-leader ChatGPT.
Not to be missed.
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–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
The post The Number One Users of ChatGPT: Students appeared first on Robot Writers AI.