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connectinfosoftech · 30 days

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Artificial Intelligence and Machine Learning Solutions by Connect Infosoft Technologies

We offer customizable AI and ML solutions tailored to meet the specific requirements of each client, ensuring maximum impact and ROI.

Let's make your business more efficient and successful with AI and ML solutions

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xceloreconnect · 5 months

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How is AI-Centered Product Design Different?

At Xcelore, our AI-Centered Product Design service stands out through the seamless integration of artificial intelligence in every phase of the design process. Unlike traditional approaches, we employ advanced Generative AI algorithms to analyze user data, predict behaviours, and dynamically adapt design elements. Our focus is on delivering a personalized and optimized user experience, anticipating user needs through machine learning insights. With Xcelore's AI-Centered Product Design service, you can expect a product that evolves based on user interactions and feedback, ensuring maximum user satisfaction and overall product effectiveness.

#artificialintelligence #economy #GenerativeAI #XceloreTech #ProductDesign #AI #AIProducts #aiServices #machinelearning #machinelearningalgorithms

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briskwinits · 9 months

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At BriskWinIT, we specialize in providing cutting-edge AI services that take advantage of the interaction between these technologies to provide opportunities in a number of industries that were previously imagined.

For more, visit: https://briskwinit.com/generative-ai-services/

#generativeai #ai #promptengineering #briskwinit #myth&fact #aiservices #potentialai

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basalanalytics · 2 years

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#artificialintelligencedevelopmentcompany #artificialintelligencedevelopment #business #artificialintelligence #artificialintelligencecompany #aidevelopmentservices #aiservices #ai #aidevelopment

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govindhtech · 11 days

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Cloud Run Accelerates AI Application Production Release

Google Cloud Run

It’s no secret that Cloud Run provides one of the easiest methods available for deploying AI-powered applications into production, freeing developers from the burden of managing the underlying infrastructure or scaling from a small number of users to millions. However, did you know that a lot of clients also choose Cloud Run as their go-to platform for giving their AI researchers the resources they require to carry out and scale up their experiments outside of their reliable Python notebooks?

Upon top of the container runtime, Cloud Run offers several services that provide an all-inclusive platform for developing and executing AI-powered apps. Google Cloud outlines several of Cloud Run’s primary capabilities in this blog post, which can expedite the creation of AI-powered applications:

Time to market: by quickly transitioning from Vertex AI Studio prototyping to a deployed containerised application

Observability: by the use of Google Cloud observability technologies and the integrated SLO monitoring of Cloud Run

Rate of innovation: test several iterations of your service concurrently with updates and traffic division

Building RAG implementations by securely and immediately connecting to cloud databases is a relevant and factual approach.

By placing several Cloud Run services in front of a single external global application load balancer, multi-regional deployments and HA are made possible.

From using AI Studio for prototyping to releasing a Cloud Run service

Vertex AI Studio is the starting point for many new AI-based products since it enables quick prototyping on a variety of models without requiring the creation of code. From there, a convenient shortcut for converting experiments into code in a number of well-known programming languages is provided by the “Generate Code” feature.

A script that calls the Vertex AI APIs that provide the AI service makes up the resulting code snippet. The process of converting that script into a web application may be as simple as transforming the hardcoded prompt into a templated string and enclosing everything in a web framework, depending on the kind of application you are attempting to develop. This may be accomplished, for instance, in Python by enclosing the prompt in a little Flask application and parameterizing the request with a straightforward Python f-string:

Google Cloud can already containerise and launch its application with the help of a straightforward package.txt file that contains the necessary requirements. Google Cloud doesn’t even need to supply a Dockerfile describing how Google Cloud containers should be generated because of Cloud Run’s support for Buildpacks.

Use telemetry and SLOs to track the performance of your application

Ensuring that the programme satisfies user expectations and determining the business impact it generates are dependent on the implementation of observability. Out of the box, Cloud Run provides both observability and monitoring of Service Level Objectives (SLOs).

In order to manage your application based on error budgets and use that measure to strike a balance between stability and rate of innovation, it is crucial to monitor SLOs. SLO monitoring can be established using Cloud Run based on configurable metrics, latency, and availability.

In order to gather all the necessary data in one location, traditional observability such as logging, monitoring, and tracing is also readily available out of the box and seamlessly integrates with Google Cloud Observability. In particular, tracing has shown to be quite useful when examining the latency decomposition of AI applications. It is frequently applied to enhance comprehension of intricate orchestration situations and RAG implementations.

Quick invention combined with simultaneous updates and cloud deployment

Numerous AI use cases drastically alter Google Cloud’s problem-solving methodology. The end result is frequently unpredictable due to the nature of LLMs and the effects of variables like temperature or subtleties in prompting. Thus, being able to conduct experiments concurrently can facilitate rapid iteration and innovation.

With Cloud Run, developers may run multiple concurrent versions of different service revisions at once and have fine-grained control over how traffic is shared among them thanks to the built-in traffic splitting feature. This could entail serving various prompt iterations to various user groups and comparing them based on a shared success metric, such as click-through rate or likelihood of purchase, for AI applications.

A managed service called Cloud Deploy can be used to automatically plan the release of several iterations of a Cloud Run service. Additionally, it connects with your current development routines such that push events in source control can initiate a deployment pipeline.

Establishing a connection to cloud databases to incorporate company data

A static pre-trained model may not always be able to produce accurate results due to the absence of the domain-specific context. Retrieval-augmented generation (RAG) and other methods of adding extra data to the prompt frequently help give the model adequate contextual information to improve the relevance of the model’s responses for a given use case.Image Credit to Google Cloud

In order to use cloud databases like AlloyDB or Cloud SQL as a vector store for RAG implementations, Cloud Run offers direct and private connectivity from the orchestrating AI application. Cloud Run may now connect to private database endpoints without the additional step of a serverless VPC connector thanks to direct VPC egress capabilities.

Deployments across several regions and custom domains

Every Cloud Run service by default gets a URL in the form of <service-name>.<project-region-hash>.a.run.app, which can be used to make HTTP queries to the service. Although this is useful for internal services and rapid prototyping, it frequently causes two issues.

Firstly, the domain suffix does not correspond to the service provider, and the URL is not very memorable. As a result, users of the service are unable to determine whether it is a genuine offering. Not even the SSL certificate, which is granted to Google, divulges who owns the said service.

The second issue is that various areas will have different URLs if you grow your service to multiple regions in order to offer HA and lower latency to your distributed user base. This implies that changing service regions is not transparent to users and must be handled at the client or DNS level.

Both of these issues may be resolved with Cloud Run’s support for custom domain names and its ability to combine deployments of Cloud Run across several regions under a single external IP address based on anycast, all behind a global external load balancer. After setting up the load balancer and turning on Cloud launch’s outlayer traffic detection feature, you can launch your AI service with a custom domain, your own certificate, and automated failover in the event of a regional outage.

Let your AI software be powered by Cloud Run

Five key areas were examined by Google Cloud, which makes Cloud Run an ideal place to start when developing AI-powered applications on top of Vertex AI’s robust services.

Read more on govindhtech.com

#CloudRun #GoogleCloud #VertexAI #VPC #cloudSQL #SQL #CloudComputing #LLM #AI #aiservices #artificialintelligence #AIapplications #news #technews #technology #technologynews #technologytrends #govindhtech

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botgochatbot · 15 days

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No more losing potential customers due to off-hours! If a customer clicks through your Google ad only to find out you're closed, you might have just lost a lead. With a chatbot, your visitors get an immediate response regardless of the time, increasing the chances of a successful conversion. Don’t leave your customers waiting! Let us show you how. 𝐒𝐰𝐢𝐭𝐜𝐡 𝐭𝐨 𝐁𝐨𝐭𝐠𝐨 𝐍𝐨𝐰! 𝗖𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐭𝐨𝐝𝐚𝐲 𝐟𝐨𝐫 𝐚 𝐟𝐫𝐞𝐞 𝟔𝟎 𝐝𝐚𝐲𝐬 𝐭𝐫𝐢𝐚𝐥, 𝐃𝐞𝐦𝐨 & 𝐐𝐮𝐨𝐭𝐞𝐬! 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻:👇 🌐𝗩𝗶𝘀𝗶𝘁 𝗨𝘀: https://botgo.io

#chatbot #crm software #aiservices #botgo by globtier

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calpionusoftwaretechnologies · 4 months

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Healthcare Chatbots: A Smart Solution for Healthcare Efficiency

Undoubtedly, technology has changed our lives to an extent where information is available at the click of a mouse or tap on your phone. Google has been the first source of information for all our queries, and with AI integration - ChatGPT, with its latest versions, has adopted a form of search console as well.

When humans fall sick or need urgent assistance regarding their healthcare needs, it is only. Still, it is natural for us to google our symptoms and reach a conclusion; however, with automated bots ready with equipped information and speed of no other, it is a rather reliable solution that healthcare organizations would intend to adopt to their programs.

Chatbots are known for their reliability and quick answers in times of adversity when you need immediate assistance with tasks that do not necessarily involve human intervention. Our latest blog highlights the technological advancements of Chatbots and their role in increasing healthcare efficiency, with patient engagement as a major benefactor.

medical ai chatbots

Patient engagement is at the crux of every healthcare organization due to its increasing impact on patient retention. A healthcare chatbot can improve the patient's experience in various ways, such as making tasks easier for patient appointments, filling prescriptions, as an assistant, etc. Conversational chatbots are all the rage due to hyper-personalization, human-like conversation experience, and speed.

Some of the factors that drive the need for chatbots in healthcare are:

Patients

Providers

Insurance providers

Bots handle 70% of conversations and do not require human assistance, saving around 2.5 billion hours, which can be further utilized for better care and patient support chatbots.

Use Cases of AI Chatbots in Healthcare

Many people realize that adopting automation into their innovation strategy can be a game changer by cost-effectively improving operations throughout the organization, benefiting employees and patients. Embracing new technologies, such as robotic process automation with chatbots in healthcare, is crucial to achieving the interdependent goals of cost reduction and better patient care.

Appointment Scheduler: Patients can chat with chatbots to book appointments with their desired hospitals without waiting on calls or queues like in old times.

Patient Queries: AI chatbots can answer single and multiple queries by patients with a high response rate and accuracy.

Text Reminders/ IVR calls: Patient text reminders and IVR calls remind patients of their due dates of billing and appointments so there are no-shows or loss of collection for the providers.

Diagnostic Chatbots: Preliminary symptoms can be diagnosed using medical picture analysis. Medical AI chatbots can assist patients in better understanding their conditions, making efforts to adhere to prescribed regimens, and following up with healthcare physicians.

Patient Billing Chatbots: With Chatbots as patient engagement software, clinicians may easily contact patients and remind them of their monthly statements through E-statements and text reminders.

A/R calling bots, often known as accounts receivable chatbots, are automated systems that collect outstanding customer payments. Businesses utilize them to automate repetitive tasks like reminder calls, payment schedules, and negotiations.

Remote Monitoring Assistants: Once a patient has completed their medical consultation and is concerned about the side effects, chatbots are utilized to follow up via remote monitoring patient support with a polite conversation. These bots even provide updates on their sessions, lab results, symptom checks, and future progress. These interactions enable patients to quickly resolve their questions and alleviate worry, thus improving their experience.

Advantages of Healthcare Chatbots

Healthcare chatbots that apply the use cases above give providers numerous cost and time savings benefits and a competitive advantage. By 2027, chatbots in healthcare are expected to become the major channel for customer service in one-quarter of enterprises.

24/7 Availability

Data-driven Insights

Resource Allocation

Patient Engagement

Overall cost reduction

Streamline Operations & Increase ROI

Patient Satisfaction

Reducing workforce via automation

How to Successfully Implement a Healthcare Chatbot

The growth of AI and Machine Learning algorithms makes AI chatbot training and implementation easier, resulting in better healthcare outcomes. Statistics indicate that aural and visual features will be merged in the future. This technology encourages organic interactions, boosts user engagement, and makes chatbots in healthcare more accessible. However, enterprises must take specific procedures before installing a chatbot, and Calpion Inc. is your go-to tech bot.

Calpion Inc. provides its complete support with the implementation, management, and deployment of a healthcare chatbot in all its stages via the following steps:

Define your chatbot purpose.

Choosing the right model with the customized model

Regulatory Compliance of HIPAA compliance and SOC-certified.

Map your Patient Journey

Train & improve your Chatbot.

Integrate within existing systems.

Conclusion

Calpion Inc. consistently deploys, maintains, and manages the solutions to ensure flawless functionality while preserving operational efficiency. Healthcare firms may improve patient experience, staff efficiency, resource allocation, and service quality by tailoring chatbots to specific hospital bottlenecks and maximizing their impact.

If you're looking for an AI-powered chatbot or a customized model with unique business requirements, Calpion Inc. is here to solve your challenges. Contact Calpion to learn how our customized AI solutions have helped our healthcare clients improve their productivity, reduce patients waiting time by 1/10th, and enrich their patient experience.

#aihealthcare #Healthcareaichatbot #AIchatbot #healthcare #aiservices #technology #digital transformation

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connectinfosoftech · 5 days

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Innovate and Grow with AI and Machine Learning Solutions!

We are your partner in leveraging AI and Machine Learning to drive business success.

Our customized solutions can help you automate processes, gain deep insights, and enhance operational efficiency.

Ready to transform your business? Contact us for a FREE consultation today!

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pranathisoftwareservices · 4 months

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Pranathi Software Services excels in exploring and implementing the diverse Uses of Artificial Intelligence. Their approach is transformative, impacting various industries by integrating innovative intelligence into key operations. The company's expertise in AI extends to solving complex business challenges, enhancing efficiency, and driving growth. By harnessing the power of AI, Pranathi Software is at the vanguard of bringing about a technological revolution in the business world.

#artificialintelligence #Uses of AI #aiservices

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yespoonamsoni · 7 months

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AI Development Solutions (Artificial Intelligence)

Pioneering AI Development Solutions, customized to your specific requirements. Our advanced Artificial Intelligence consulting services leverage cutting-edge technology, empowering your business with intelligent, strategic decisions. Stay ahead of your competitors by visiting OnGraph and experience the power of Natural Language Processing, enabling seamless communication and interaction.

#ai consulting #artificialintelligencesolutions #AIservices

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copperchips · 10 months

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AI in Healthcare: Pioneering the Future of Medicine

Artificial Intelligence (AI) has emerged as a game-changer in the field of healthcare, reshaping the way we approach medical diagnosis, treatment, and patient care. With the ability to analyze vast volumes of patient data at incredible speeds, AI is ushering in a new era of precision medicine and healthcare innovation.

#AIServices #ArtificialIntelligence

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govindhtech · 3 months

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Claude 3 Sonnet on Amazon Bedrock & Google Cloud

Amazon Bedrock offers Anthropic’ s Claude 3 Sonnet foundation model AWS and Anthropic announced a strategic partnership in September 2023. The partnership combined their respective technological prowess and knowledge of safer generative artificial intelligence (AI) to expedite the creation of Anthropic’s Claude foundation models (FMs) and enable broad AWS customer access to them. You can expedite your generative AI journeys, redefine user experiences, and reinvent your enterprises with early access to Anthropic’s Claude model capabilities in Amazon Bedrock.

Amazon Bedrock made Anthropic’s Claude 2.1 available in November 2023. This tool gives businesses essential tools to develop generative AI. A 200,000 token context window, decreased rates of hallucinations, enhanced accuracy across lengthy texts, system prompts, and a beta tool usage capability for workflow orchestration and function calling are all included in Claude 2.1.

Anthropic today unveiled Claude 3, a new family of cutting-edge AI models that let users choose the precise balance of cost, speed, and intelligence to meet their specific business requirements. The three versions in the family are Claude 3 Opus, an extremely intelligent offering for the highest degree of performance on very complicated jobs, Claude 3 Sonnet, the perfect balance between skills and speed, and Claude 3 Haiku, the quickest and most compact device for almost instantaneous reaction.

Additionally, AWS is announcing that Claude 3 Sonnet from Anthropic is now available in Amazon Bedrock, and that Claude 3 Opus and Claude 3 Haiku will be available shortly. With improved steerability and additional image-to-text vision capabilities, the Claude 3 Sonnet model is two times quicker than the Claude 2 and Claude 2.1 models for the great majority of workloads.

Because Claude 3 Sonnet is available on Amazon Bedrock, businesses in need of intelligence, dependability, and speed may create generative AI applications at a reasonable cost. The Claude 3 Sonnet, the newest model from Anthropic, is now compatible with the Amazon Bedrock console.

Anthropic’s Claude 3 Sonnet introduction The following are some salient features of Amazon Bedrock’s new Claude 3 Sonnet model: Claude 3 has increased his pace significantly, going two times quicker. It is twice as fast with the same degree of intelligence for the great majority of workloads as Anthropic’s most successful models, Claude 2 and Claude 2.1. Claude 3 Sonnet is an obvious option for intelligent activities requiring quick replies because to its speed and competence, such as sales automation or information retrieval. Use cases such as content creation, categorization, data extraction, and precise searching across knowledge bases are included in this.

Enhanced steerability AI systems with heightened steerability provide customers more control over outputs and consistently produce better results. Refusing to respond to inquiries that touch on the boundaries of the system’s safeguards against undesirable outcomes is noticeably less common. Claude 3 Sonnet makes it simpler for developers to create corporate and frontier apps since it is more maneuverable and has improved direction following in well-known structured output formats such as JSON. This is especially crucial for corporate use cases like algorithmic decision-making in delicate industries like financial services, health and medical diagnosis, and autonomous cars.

Vision capabilities for image-to-text Claude 3 has the ability to process pictures and provide text outputs. It can analyze and comprehend technical diagrams, reports, graphs, charts, and other visual materials with remarkable proficiency. While keeping a significant speed advantage, Claude 3 Sonnet performs comparably to other best-in-class models with image processing capabilities.

Improved comprehension and response in languages other than English, such French, Japanese, and Spanish, are two benefits of Claude 3’s expanded language support. With this increased language coverage, Claude 3 Sonnet is better equipped to assist enterprises in need of sophisticated translation services as well as international organizations seeking AI services across several languages and countries. Claude 3 Sonnet is also more proficient in coding and mathematics, as shown by Anthropic’s results on assessments like Codex (HumanEval) and grade-school arithmetic problems (GSM8K and Hendrycks).

Introducing Anthropic’s Claude 3 Google Cloud Vertex AI models Google Cloud is to enable client choice and creativity by providing Vertex AI with a carefully selected selection of first-party, open-source, and third-party models. They are happy to share that Claude 3, Anthropic’s latest family of cutting-edge models, will be widely accessible in Vertex AI Model Garden in the next weeks. One of the models will even be available for private preview access beginning today.

Claude is Anthropic’s next-generation artificial intelligence assistant, which aids in work management for businesses of any size. With the introduction of Claude 3, Anthropic offers a family of three unique models that are tailored for different corporate applications:

Claude 3 Opus: The most advanced and wisest model produced by Anthropic.

Claude 3 Sonnet: The optimal blend of Anthropic’s agility and dexterity.

Claude 3 Haiku: The smallest and quickest model made by Anthropic.

While Haiku is Anthropic’s quickest and most affordable model, Claude 3 Opus and Sonnet both provide better reasoning than previous Claude iterations when it comes to complicated jobs, content production, scientific questions, arithmetic, and coding. Better fluency in non-English languages and visual skills that enable activities like picture metadata creation and insights extraction from PDFs, flow charts, and a variety of other formats are features shared by all Claude 3 models.

Google Cloud users will have the option to choose among all three Claude 3 models in the coming weeks via Vertex AI Model Garden’s API access. Additionally, clients may now apply for exclusive access to Claude 3 Sonnet in Model Garden via private preview.

Construct and implement in Vertex AI using Claude 3 Google will work together to provide their clients with access to Anthropic’s most recent models via Vertex AI, their all-inclusive AI development platform. With the Claude 3 family joining the more over 130 models currently in the Vertex AI Model Garden, customers will have even more options and flexibility as new AI application cases continue to develop at a fast pace.