Category: IA (EN)

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AI Agents with MCP: Transformative Enterprise AI Within Reach

AI Agents with MCP: Transformative Enterprise AI Within Reach

Model Context Protocol (MCP) is an open standard designed to connect any AI agent to your data and tools in real time, making it more effective and relevant. Launched in November 2024 by Anthropic—the company behind the Claude AI service—MCP defines a common language to guide the AI to the right sources and actions, whether it’s an in-house model (custom AI hosted on-premises) or a third-party API such as ChatGPT or Claude. This enables the AI to interact with multiple systems and deliver much broader capabilities. For decision-makers and technology leaders, MCP means rapid deployment of intelligent (or AI assistant) agents that are contextually relevant and secure, without sacrificing business agility or increasing technical debt.

MCP: A Contextual Protocol for Ecosystem-Connected AI

The MCP protocol stands apart from classic approaches by standardizing exchanges between AI and enterprise systems, providing instant, secure access to business data and automated triggers within your IT landscape.

MCP acts as a universal translator: it turns an AI agent’s request into calls to databases, CRMs, ERPs, document repositories, or any other part of your IT stack, then returns structured context to the model. Where every new integration once required bespoke code, MCP lets you build one connector that works with all compliant tools. This openness accelerates evolution of your system while minimizing maintenance costs.

By choosing a widely adopted open-source standard like MCP, you avoid vendor lock-in and retain full control over your connectors and models. Plus, the MCP community continuously enriches adapters—whether for enterprise AI platforms or open-source frameworks—ensuring sustainable interoperability. Today, this standard has become essential for anyone integrating AI into their business processes and value chain.

High-Performance, Scalable, Customizable, and Secure AI Agents

MCP enables you to build intelligent agents that draw on real-time data from your key systems and orchestrate complex processes, while delivering modularity, scalability, and security.

Here are some examples of what MCP can bring to organizations that integrate it effectively:

  • Performance & Relevance
    MCP-powered agents can query your CRM, document management system, or application logs to generate context-aware responses, greatly increasing the business relevance of model outputs.
  • Scalability
    The standard protocol makes it easy to scale (adding new sources, handling increased traffic) without a full redesign—offering flexibility and true scalability.
  • Customization
    Each agent can be configured to access only the required business data and actions, optimize its tone and governance rules, and comply with regulatory requirements. This boosts flexibility and contextualization of your model.
  • Security
    MCP includes built-in authentication and auditing mechanisms under your control. No black-box data flows—every exchange is logged and access-restricted according to defined permissions. In Switzerland, and particularly in AI contexts, this level of security is crucial.

Enterprise Use Cases for MCP

From customer support to cybersecurity, and from administrative processes to IT operations, MCP powers AI agents that precisely address your business challenges.

  1. Customer Support
    Deploy a virtual assistant that consults the CRM and knowledge base in real time. Contextualized replies can cut first-level ticket volume by up to 30 %.
  2. HR/IT Automation
    An “Onboarding” agent can automatically create user accounts, send welcome emails, and update the ERP based on an HR form—freeing IT from repetitive tasks.
  3. Proactive Industrial Maintenance
    An MCP agent monitors critical machine metrics (or servers) via SCADA, IoT, or supervision systems, predicts failures through trend analysis, and auto-generates preventive maintenance orders in a CMMS—reducing unplanned downtime by 20 %–40 % and extending equipment life.
  4. Cybersecurity
    An automated watcher correlates SIEM alerts and event logs, notifies analysts, and suggests actionable remediation plans—improving average response times by 40 %.
  5. Business Intelligence
    A conversational tool can query your data warehouse and reporting systems to deliver on-demand dashboards and ad-hoc analyses without mobilizing data analysts.

These five examples are generic; the possibilities are endless and depend on each company’s challenges and resources. While standalone AI could automate certain time-consuming tasks, MCP supercharges automation by enabling AI to understand context, personalize its work, and interact precisely with its environment—making it far more effective in handling parts of your value chain. MCP will therefore play a key role in task automation and optimization in Switzerland and internationally in the coming months and years.

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How MCP Works (For Technical Readers)

MCP relies on exchanging JSON messages between the AI agent and business connectors, orchestrated by a lightweight broker:

  1. Initial Request
    The user or application sends a question or trigger to the AI agent.
  2. Context Analysis
    The agent, equipped with an appropriate prompt, wraps the request in an MCP envelope (with metadata about the user, application, permissions).
  3. Broker & Connectors
    The MCP broker reads the envelope, identifies required connectors (CRM, ERP, document store, etc.), and issues REST or gRPC API calls per a simple, extensible specification.
  4. Data Retrieval & Aggregation
    Connectors return structured fragments (JSON, XML, protobuf), which the broker assembles into a single, rich context.
  5. AI Model Invocation
    The AI agent receives the full request and context, then queries the model (hosted locally, in your private cloud, or via an API such as OpenAI) to generate the response or next actions.
  6. Execution & Feedback
    For action steps (ticket creation, email dispatch, etc.), the broker relays commands to target systems and can return an execution log for auditing.

This workflow is completely vendor-agnostic: you can host an open-source speech-to-text model in-house for call center interactions, or use the OpenAI API for NLP, depending on business context and cost or time constraints.

Challenges & Best Practices for Successful MCP Deployment

To guide technical and business teams through concrete implementation of the protocol while anticipating key pitfalls, we recommend following these steps:

1. Define Your Functional Scope

  • Map priority use cases (customer support, maintenance, BI…)
  • Identify target systems (CRM, ERP, SCADA…) and access constraints (authentication, throughput, latency)

2. Governance & Security

  • Establish fine-grained access policies: which agents can query which data, under what conditions
  • Implement continuous MCP call auditing (centralized logs, anomaly alerts)

3. Technical Pilot & Rapid Prototyping

  • Start with a PoC on a simple case (e.g., CRM-connected FAQ assistant)
  • Measure end-to-end latency and functional enrichment delivered by MCP

4. Industrialization & Scaling

  • Deploy a resilient MCP broker (high availability, load balancing)
  • Version and test business adapters (unit/integration tests)

5. Continuous Monitoring & Optimization

  • Dashboards tracking:
    • Number of MCP calls per day
    • Average response time
    • Error or integration-failure rate
  • Collect user feedback (internal NPS) to refine and prioritize new connectors

Edana’s Approach: Flexible Solutions

Edana combines the best of open source, third-party APIs, existing tool integration, and custom development to address each business context.

We naturally favor open standards and open-source building blocks to limit costs, avoid vendor lock-in, and optimize total cost of ownership. However, when time-to-market, budget, or complexity constraints demand it, we integrate proven solutions: hosting an open-source speech-to-text model for call centers, leveraging the OpenAI API for rapid NLP understanding, or coupling with a third-party computer-vision service… With MCP, these elements mesh seamlessly into your ecosystem without adding technical debt.

Our methodology applies a variety of technology approaches tailored to maximize ROI and ensure robustness and longevity of your solutions.

As ecosystem architects, we prioritize security, scalability, and sustainability across all your AI agent platforms. We factor in your CSR commitments and corporate strategy to deliver responsible, high-performance AI aligned with your values and specific business needs—accelerating your digital transformation without compromising on quality or data control.

Ready to automate your business processes without sacrificing quality—in fact, improving it? Not sure where to start? Our experts are here to discuss your challenges and guide you end-to-end.

Discuss about your challenges with an Edana expert

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Can Machine learning be used in Web Development?

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Can Machine learning be used in Web Development?

The world is becoming more and more electronic. Our fantasies become real every year. We live in a futuristic movie world that gets better. Machine learning is one of the most significant achievements. 

What is Machine Learning?

Machine Learning is a part of Artificial intelligence (AI). Artificial intelligence Machine learning is a field that explores new ways of “learning.” Machine learning is responsible for producing an algorithm that uses specific data, makes predictions, and chooses. It is used in many apps and provides algorithms when traditional ways do not develop beneficial results.  

Machine learning programs are unique because they can perform tasks without being programmed to do them, so machine learning is close to computational statistics; the main idea, which is to make predictions by data analysis, is the same for both of them. 

Machine learning incorporates different approaches to completing assignments. One of the most popular is supervised learning in which the control algorithm holds the correct answers to the given questions.

For example, in AI brought to learn characters, developers often use the handwritten symbol database (MNIST), popular in the field. This allows them to compare the responses of the AI algorithms they are learning with what they should be, to see which ones work best.

Using Machine learning in Website development

Machine learning is increasingly popular in any technology field because it improves the performance of algorithms and programs. Because of this, Machine learning is one of the best choices when you want to upgrade your website to its finest. 

If you are searching for a better user interface, to improve the website’s protection, or upgrade the monitoring system, consider using Machine Learning for your Web. 

It is not only desirable but crucial for Web developers to consider and focus on Machine learning because it makes the site efficient, functional, and user-friendly on mobile and desktop devices. In addition, machine learning implements automated chat usability, improves technological intelligence, and boosts user experience.

Benefits of Machine learning

When developers use machine learning in their development processes, time-consuming, complex, and complicated tasks becomes a job for algorithm and is done only in seconds. Plus, information and charges are more accurate, and all doubts are eliminated. Here are some most important benefits that can be obtained from incorporating Machine learning into the workflow. 

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Examine customer attitude

Machine learning systems can be used to track and research users’ needs and behaviors. The algorithm allows having all the information in no time and using it to upgrade your customer experience. Abolish unnecessary things, and answer your customers’ needs faster and more effectively. 

Flexible data collecting

Machine learning is impressive because it can do everything traditional methods do, but it also automates tasks and gives more accurate answers. For example, before Machine learning systems, collecting data was done manually and wasn’t perfect. But ML systems figure out what type of information is essential for your project, collect them automatically, and give you in little time. 

Guarantee security 

At this time, cyber-attacks are not a rare thing, all the data Machine learning systems can collect must be secured and safe. Machine learning can store all the information defended. It prevents attacks and allows you to track the algorithm that does that. That’s why it is twice secured. 

Marketing strategy

No matter how surprising it is, using Machine learning on your Web applications will help you to upgrade your marketing strategy. One of the main features of Machine learning systems is predictions; it will forecast your customers’ choices and plans based on their activity. This kind of information can be used to boost retention and purchases. 

Wrapping up 

Automating simple tasks isn’t new, but Machine learning makes complicated tasks automated; this is why Machine learning systems are innovative and the future of technology. It already has a significant influence on web development which will grow with the years and new creations. 

What We Offer

For more similar articles, make sure to scroll through our Publications on Edana. Our expertise includes Web development Services, software and AI engineering, digital consulting and IT systems architecture. Feel free to contact us anytime.

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What Role Does AI Play In Marketing?

What Role Does AI Play In Marketing?

Artificial intelligence (AI) plays a huge role in our lives. It entered in many businesses and made the work easier and also managed to become a part of marketing world. AI marketing is a method that uses technology to improve the customer journey.

Benefits of AI marketing

AI has many benefits. First of all, up until now conducting analysis and discussing the data was time-consuming, but now you can have a software that does the analysis and gives you the data. This way you can generate more return on investment and your colleagues will have more time to focus on more pressing matters.

Secondly, it works on anything as long as you have a data to process. This means that you can have a good grasp of your customer behavior patterns and make decisions accordingly.

Lastly, it can reduce costs. It frees up capital and helps you use it on other company divisions to increase reliability.

15 tips to boost you AI marketing

As a marketer you can use AI to ease your workload. There are 15 tips of how you can work smarter and not harder:

  • Predict customer behavior

We can all agree that there is no business without a customer. You may ask “How can I keep my customers if I don’t know what they want?” The answer is to know how they use your product, follow small trends and you will see everything. For this you need AI. Amazon is the best example of it. With the right algorithm it checks customer behavior and offers product that they will be interested in.

  • Decrease AMPs load time

We all have been in a situation where you are trying to purchase product online, but the webpage is not loading, so you get annoyed and just leave. With the help of AI you can easily solve this issue. For example google has the fastest load time using AI algorithm.

  • Provide a personalized user experience

At some point we all have an experience with chat bots, they can be helpful when providing general information, but receiving the same answer all the time is pretty annoying and in certain situations they prove to be useless. This happens because they are not real AI and at the end if you want to address an issue you have to speak with a real human. With real AI you would naturally deliver the desired answers.

For example, Sephora is using AI to help customers in various ways including scheduling an appointment.

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  • Create a content

You can take advantage of AI and use it to create content for your brand. You can create blog posts to drive traffic to your site and boost your SEO.

  • Boost sourcing accuracy

Generating leads is one thing but checking their validity is another. AI can look at all the data you have collected from various perspectives and determine which one will lead to success.

  • Predict customer churn

Prediction helps with prevention. After analyzing all the information artificial intelligence will be able to determine the reason behind customer churn and find solution before it is too late.

  • Profitable dynamic pricing models

When you have multiple store locations it is difficult to check the performance of them all, but AI can monitor it and notify you if there is a decrease of performance and find a solution to the issue.

  • Sentiment analysis

SA helps you see how your company, product and service is perceived. This helps you fix any issue that may arise and make changes almost immediately in the process.

  • Improve website experiments

If you want to check the location that will have the best response to your website before officially releasing it, you can use AI. It can determine which location will be more receptive to new features and will give you feedback on what to improve.

  • Prioritize ad targeting and personalization

Collecting data plays a huge role in having a successful business. You can use data to determine faster and efficiently what to do with ads. AI can find patterns that you might not notice and give you an insight.

  • Relevant recommandation system

When you have a huge amount of products it is difficult to connect each customer to the right one and keep driving customer retention. AI can easily find connection between the product and a consumer and run common threads between them.

  • Smart email content curation

If you want to keep your customers, you should stop sending irrelevant mass emails. AI helps you choose the content of the email based on the thing customers care about.

  • Interpret custom loyalty card data

Above we spoke about the importance of data collecting, but exactly where does this data come from? The best way to get it is by tracking the rewards or loyalty systems. This is a great way to see the patterns and make successful business deals.

  • Computer vision for image and object recognition

AI can be used to eliminate time-consuming manual tasks. You can use computer vision algorithm to sort through thousands of pictures and videos placed in social media. It has a good accuracy and offers client’s specific product that they are interested in.

  • AI-enhanced PPC

Artificial intelligence can help you discover new channels of advertisement, what’s more, when you make AI responsible for picking keywords, your PPC campaigns will be automatically updating.

Conclusion

AI is a very powerful tool, which can reduce your workload and increase productivity. There are many companies that are successfully using AI marketing to work efficiently and gain more profits.

So are you using AI and if not, what are you waiting for?