HowFoundation Models vs LLM ( VLM) differ and which IT Giant to select for...

Understanding Foundation Models (FM) and Large Language Models (LLM) Across Cloud Providers

With the rapid advancements in AI, cloud providers like AWS, Azure, Google, NVIDIA, and Meta offer powerful Foundation Models (FM) and Large Language Models (LLM) to cater to various business needs. This article aims to provide a clear understanding of these offerings, their pricing models, and specific LLMs for video tagging generation—essential knowledge for a pre-sales team.  

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AI Offerings by Major Cloud Providers

A. Google Cloud (Vertex AI & Gemini)

Foundation Models (FM):

Gemini → A multimodal AI model capable of handling text, images, audio, and video.  

Vertex AI → A platform offering access to foundation models, including Gemini.  

Large Language Models (LLM):

 Gemini Pro → Optimized for text-based applications like chatbots, summarization, and code generation.  

Pricing Model:

 Vertex AI Pricing: Based on API usage and model type (on-demand or provisioned throughput).  

  Gemini Pricing: Pay-per-token usage.  

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B. Amazon Web Services (AWS)

Foundation Models (FM):

 Amazon Bedrock → Provides access to multiple FMs from partners like Anthropic, AI21, and Meta.  

  Titan Models → Amazon’s proprietary foundation models.  

Large Language Models (LLM):

Titan Text → Amazon’s text generation model.  

 Claude (Anthropic), Llama (Meta), Jurassic (AI21) → LLMs available via Bedrock.  

Pricing Model:

Bedrock Pricing: 

    - On-Demand & Batch: Pay-per-use.  

    - Provisioned Throughput: Subscription-based pricing for enterprise needs.  

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C. Microsoft Azure  

Foundation Models (FM):

 Azure OpenAI Service → Hosts OpenAI's foundation models.  

 Phi-2 → Microsoft’s smaller-scale foundation model.  

Large Language Models (LLM):

  - GPT-4, GPT-3.5→ OpenAI’s advanced LLMs hosted by Azure.  

  -Turing-NLG → Microsoft’s in-house NLP model.  

Pricing Model:

  - Azure OpenAI Pricing: Pay-per-token for API usage.  

  - Custom Model Training: Additional costs apply.  

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D. NVIDIA AI

Foundation Models (FM):  

  - NVIDIA AI Foundation Models→ Includes models for text, vision, and scientific applications.  

  - Nemotron-3 → A general-purpose foundation model.  

Large Language Models (LLM):

  -Nemotron-3 8B → NVIDIA’s LLM for text generation.  

  -BioNeMo → LLM focused on biomedical applications.  

Pricing Model:

  - Pricing depends on GPU usage and API call volume (available upon request).  

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E. Meta (Facebook AI)

Foundation Models (FM): 

  - Llama Models (1, 2, 3) → Meta’s open-source foundation models.  

Large Language Models (LLM):

  - Llama 2, Llama 3→ Designed for text-based AI applications.  

Pricing Model:

  - Available via Azure, AWS, and Google Cloud with platform-dependent pricing.  

Conclusion

For pre-sales teams, understanding which AI model best fits a business need is crucial:  

- If you need a multimodal AI solution (text, images, video, code), opt for a Foundation Model like Gemini, Titan, or Nemotron.  

- For text-only applications (chatbots, summarization, translation), consider LLMs like GPT-4, Claude, or Llama 2.  

- For video tagging applications,Gemini Pro Visio , TagGPT and Azure AI Video Indexer are leading solutions.  

Each cloud provider offers different pricing models, so choosing the right cost-effective solution depends on usage, scalability, and enterprise needs.  

Steps for Pre-Sales Teams:

✅ Understand client requirements (multimodal vs. text-based AI).

✅ Compare pricing models for cost-efficiency.

✅ Recommend the right cloud provider based on model availability and integration needs.

By leveraging these insights,  teams can effectively identify AI solutions tailored to their needs.

AI Agents vs. Traditional RPA: How Google, Salesforce, and Microsoft Are Redefining Business Automation

AI Agents vs. Traditional RPA: What's the Difference?

Automation is transforming businesses by speeding up processes, making them more efficient, and reducing errors. Two key players in this space are Traditional Robotic Process Automation (RPA) and AI Agents. While both help with automation, they do so in different ways and for different types of tasks. Let’s break down the differences in simple terms, along with examples of AI tools from Google, Salesforce, Adobe, and Microsoft that surpass RPA in various use cases.

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What Is Traditional RPA?

Traditional RPA uses software bots to perform repetitive, rule-based tasks that humans usually do. These tasks are predictable and follow a specific pattern, such as:

• Data Entry: Entering customer information into a database.
• Invoice Processing: Moving invoices from email to a finance system.
• Report Generation: Automatically creating daily sales reports.

RPA bots mimic human actions like copying and pasting data or filling out forms. However, RPA struggles with tasks involving unstructured data (like free text) or situations that change often.

Examples of Traditional RPA Use Cases:

• Finance: Automating account reconciliations.
• Healthcare: Scheduling patient appointments.
• Retail: Managing inventory updates.

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What Are AI Agents?

AI Agents are more advanced. They use Artificial Intelligence (AI) to learn, reason, and adapt to new situations. Unlike RPA, they don’t need detailed instructions for every task. They can handle complex tasks by understanding data and making decisions.

Examples of AI Agent Use Cases:

• Customer Service: Chatbots that understand and respond to customer inquiries.
• Fraud Detection: Identifying unusual patterns in financial transactions.
• Predictive Maintenance: Analyzing machine data to predict when maintenance is needed.

AI agents use technologies like Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision to perform tasks. They can work with unstructured data like images, audio, and free text.

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AI Tools from Tech Giants That Surpass RPA

1. Google's AI Tools:

   • Google Cloud AI: Offers tools like AutoML and Dialogflow, Vertex AI powered Gemini Models which help in building intelligent chatbots and processing large datasets. These tools can learn and improve, unlike RPA, which only follows predefined rules.
   • Google Vision AI: Can analyze images and extract insights, something traditional RPA cannot handle.

2. Salesforce’s AI:

   • Salesforce Einstein: This AI platform helps with predictive analytics, customer sentiment analysis, and automated recommendations, making it far more versatile than traditional RPA bots.

3. Adobe's AI:

   • Adobe Firefly,Sensei: Powers intelligent services in Adobe’s creative and marketing tools. It helps in automating complex creative tasks, like image recognition and customer experience personalization, which are beyond the capabilities of RPA.

4. Microsoft’s AI:

   • Azure AI: Offers tools like Cognitive Services and Azure Machine Learning, which help in building intelligent applications. For example, Azure Bot Service can create chatbots that understand natural language and provide real-time customer support, surpassing RPA’s basic automation.

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Key Differences Between RPA and AI Agents

1. Task Complexity:

   • RPA: Best for simple, repetitive tasks.
   • AI Agents: Ideal for complex, evolving tasks that require decision-making. For instance, Google Dialogflow can handle dynamic conversations, unlike RPA bots that follow fixed scripts.

2. Learning Abilities:

   • RPA: Cannot learn or adapt; needs reprogramming for new tasks.
   • AI Agents: Can learn from data and improve over time. Salesforce Einstein, for example, improves customer service by learning from past interactions.

3. Data Handling:

   • RPA: Works with structured data (like spreadsheets).
   • AI Agents: Can handle both structured and unstructured data. Adobe Sensei can analyze and optimize digital content, which RPA cannot do.

4. Decision-Making:

   • RPA: Follows set rules; needs human intervention for exceptions.
   • AI Agents: Can make decisions and handle exceptions autonomously. Microsoft Azure AI services can interpret complex scenarios and act accordingly.

5. Scalability:

   • RPA: Scales by adding more bots.
   • AI Agents: Scales through advanced algorithms and cloud computing. Google Cloud AI can process vast amounts of data and scale seamlessly, unlike traditional RPA.

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When to Use RPA

Use RPA for tasks that are repetitive and rule-based, where the process doesn’t change much. It’s perfect for automating manual tasks in industries like:

• Finance: Data migration.
• Healthcare: Claims processing.
• Retail: Inventory management.

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When to Use AI Agents

AI Agents are better for tasks that require adaptability and decision-making. They’re useful in scenarios like:

• Customer Service: Personalized chatbot responses with tools like Google Dialogflow.
• Marketing: Personalized product recommendations using Adobe Sensei.
• Supply Chain: Real-time optimization using Salesforce Einstein.

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Conclusion

Both RPA and AI Agents are useful for automation but are suited for different types of tasks. RPA is great for simple, repetitive processes, while AI Agents shine in complex, dynamic environments. Often, the best approach is to use both, combining the strengths of RPA's reliability with AI's intelligence, leveraging tools from Google, Salesforce, Adobe, and Microsoft for a comprehensive automation strategy.

Salesforce's AI Revolution: How Agentforce is Transforming Business Efficiency and Reducing Costs

Salesforce: Targeting to lead AI Revolution

Salesforce, traditionally known for customer relationship management (CRM) software, is making significant strides in artificial intelligence (AI). They’ve introduced a powerful new AI tool called "Agentforce," which could transform how businesses operate and deliver customer service.

The Shift to AI Agents:

Unlike basic chatbots that only provide information, Agentforce’s AI agents can perform tasks like filing complaints, booking appointments, or updating customer details. This advancement reduces errors because the AI operates only within the data it’s trained on, making it more reliable. Salesforce claims that their AI agents will significantly reduce "hallucinations"—instances where AI generates incorrect or irrelevant information—by limiting responses to pre-approved, accurate data sources.

Controlling Hallucinations:

A key feature of Agentforce is its ability to control hallucinations effectively. Since it generates content solely from the data and sources businesses have trained it on, the risk of misinformation is minimized unlike training from openweb. This makes Agentforce more reliable than some other AI tools that pull information from the vast, and sometimes inaccurate, public internet.

Impacts on Jobs and Efficiency:

Salesforce openly acknowledges that AI tools like Agentforce can replace certain jobs, but they highlight the efficiency gains. For instance, John Wiley & Sons, an educational publisher, used Agentforce to significantly reduce customer service response times, avoiding the need to hire extra staff during busy periods. This shift allows companies to handle higher volumes of customer interactions without increasing their workforce.

Cost Advantages:

Agentforce’s new pricing model charges per conversation instead of per user, creating significant cost advantages for businesses. This means companies can scale their customer service or sales operations without corresponding increases in staff costs. By reducing the need for hiring additional employees during peak periods, businesses can lower their operational expenses while maintaining or even improving service levels. Additionally, the automation of routine tasks can free up human employees to focus on more complex and value-adding activities, further enhancing productivity.

Use Cases for eCommerce, Media, and Telecom Companies:

1. eCommerce : Agentforce can automate customer support by handling order inquiries, returns, and tracking issues, providing instant solutions that improve customer satisfaction. It can also assist in personalized product recommendations and streamline the purchasing process.

   

2. Media: Media companies can leverage Agentforce to manage subscription services, content recommendations, and user inquiries. AI agents can help in automating the distribution of content and enhancing user engagement through personalized suggestions.

3. Telecom: In the telecom sector, Agentforce can be used to handle customer service tasks such as troubleshooting, account management, and billing inquiries. This reduces the workload on human agents and allows telecom companies to offer faster, more efficient service.

 Bold Business Strategy:

Salesforce’s new pricing model for Agentforce charges per conversation instead of per user. This approach benefits companies by saving on hiring costs while still generating revenue for Salesforce, even if fewer people are employed. The cost savings from reduced staffing needs can be substantial, allowing businesses to allocate resources more efficiently.

Positioning in the AI Market:

Salesforce’s widespread use in businesses gives it a competitive edge. Their deep integration with existing business processes means companies can adopt these AI tools without overhauling their systems, making Salesforce a tough competitor for newer AI firms like OpenAI and Anthropic.

Conclusion:

Salesforce’s proactive approach to AI adoption and its potential to displace jobs show their commitment to leading in the AI space. Their strategy not only enhances efficiency for clients but also offers significant cost savings and sets a strong foundation for future growth in the evolving AI market. With applications in eCommerce, media, and telecom, Salesforce’s AI solutions are poised to drive innovation across various industries.

Insights in this article are basis some of the recent news and actual offering might differ.

AI Agents in 2025: How Businesses Can Leverage Advanced AI Workflows for Success

AI Agents in 2025: What Business Leaders Need to Know

Introduction to AI Agents for Business

In 2025, AI agents will play a crucial role in businesses. While the buzz around AI started a few years ago, many early AI projects didn't succeed because they weren’t fully integrated into business systems or lacked necessary controls. This year, that will change, although challenges remain.

A Simple Example: Email-Answering Tool

Imagine a tool that automatically drafts email replies. This simple example shows the potential of AI agents but also highlights the difficulties companies face when using AI.

Why Basic AI Tools Aren’t Enough

Many businesses used basic AI tools called GPT wrappers, which connect AI to simple interfaces. While easy to set up, these tools have major shortcomings:

- They don’t integrate with other systems (like checking your calendar).

- They lack context (like knowing your relationship with the sender).

- They have no security, guardrails, or user control.

- They sometimes provide incorrect or made-up information.

Building Better AI Solutions

Instead of using basic tools, businesses need AI agents that work within a more complex system, or workflow. This involves multiple AI models working together, much like tools in automation platforms like Gemini, Chatgpt, Zapier powered by AI.

An Improved Email Tool Workflow

A more advanced AI agent could:

1. Check your calendar to see if you're free.

2. Review past emails with the sender.

3. Predict whether you'd want to attend based on your past behavior.

4. Create several reply options for you to choose from.

This workflow addresses many of the issues with simple AI tools, by integrating with systems, providing context, and offering better control and accuracy.

Key Components of AI Workflows

For AI agents to work effectively, they need:

- System Integration: To connect with other tools like calendars.

- Context Search: To find relevant past information.

- Traditional AI: To analyze data and make predictions.

- User Design: To present choices that the user can control.

The Future of AI in Business

In 2025, AI agents will streamline and improve business processes across various industries, but there won’t be a single dominant AI tool. Instead, there will be many specialized workflows for tasks like customer service, legal support, and sales.

Conclusion

To succeed with AI, business leaders and product managers must focus on creating solutions that integrate well, provide value, and are easy to use. This approach will help businesses fully benefit from AI agents in 2025 and beyond.