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We are your implementation partner for Retrieval-Augmented Generation (RAG) solutions

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Retrieval-Augmented Generation

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As Valprovia, we have the privilege of supporting businesses ranging from small and medium-sized enterprises to large corporations with their challenges through our products and consulting services in Azure OpenAI, Microsoft Copilot, Microsoft 365 and Dynamics 365 areas.

What are the benefits of Retrieval-Augmented Generation?

Retrieval-Augmented Generation (RAG) enables businesses to leverage their own data through advanced chat-based solutions. By integrating RAG technology, companies can transform their internal documents, databases, emails, and other relevant information into a powerful, interactive chat experience.
  • Enhance Accessibility: Provide employees and customers with instant access to accurate information from your data sources through a user-friendly chat interface.
  • Improve Efficiency: Streamline workflows by allowing users to quickly retrieve and interact with relevant data without the need for complex queries or navigation.
  • Boost Decision-Making: Enable informed decisions by offering real-time, contextually accurate responses derived from up-to-date information.
  • Increase Engagement: Enhance user engagement by delivering personalized, data-driven interactions that build trust and satisfaction.
  • Expanded Use Cases: Integrates a wide range of external information, enabling AI to handle diverse prompts and applications.
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Almost two-thirds of CEOs think investing in new AI technologies without a clear business case is reasonable.


of CEOs think moving too slowly poses a greater risk than moving swiftly

[Source: AND Digital]

Chat with your own documents

Imagine that you can access your document contents via chat. Nowadays, almost all companies use Microsoft SharePoint, and the majority of their documents are stored there. For many organizations, SharePoint is a black box when it comes to document search. With our RAG approach, you can find your documents in SharePoint much faster and more efficiently. What AI cannot replace is innovation. With our RAG approach, your users can work on innovations supported by document content, ensuring your competitiveness.
Valprovia RAG (Retrieval Augmented Generation) Solution
We specialize in preparing your data and configuring RAG systems tailored to your specific needs.
Question Interpretation
Combining free-text and keyword search for a more accurate understanding of your question.
Seamless integration of vast data sources will allow you to address complex questions on your own information.
The AI combines the question and retrieved data as augmented context to create a contextually relevant response.

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๐—œ๐˜€ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ผ๐—ฟ๐—ด๐—ฎ๐—ป๐—ถ๐˜€๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—ณ๐—ผ๐—ฟ ๐—”๐—œ? Find out with our AI Readiness Score! Assessment across 5 key areas for tailored insights. Start your AI journey now!

Introducing our AI Readiness Scoreโ€”a cutting-edge analysis tool designed to assess your organization's readiness for AI implementation.

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Want to Unlock the Potential of Your Data with RAG?

We have many years of experience helping our clients structure and organize their data. This know-how, combined with our tools and experience in custom search solutions, allows us to effectively configure company-specific Retrieval-Augmented Generation (RAG) solutions.

Our AI Readiness Platform is at the forefront of our services, designed to maximize the success of RAG implementations. This platform ensures your organization is fully prepared to leverage RAG technology, enhancing data integration, management, and overall AI readiness.

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Implementing your RAG solution with Valprovia


Data Preparation

Our experts will support you to identify the status of your data. We can offer further tools to automate this process going forward.

Together we will create an inventory of your data, make it more manageable in terms of relevant context (chunking), and transform the text for AI usage (embedding).


Optimizing Search

We will customize the search interface to your needs. 

Our RAG solution interface combines keyword search and free-text questions to deliver more accurate retrieval results from your data. We will optimize the search options to best match your data and user experience.


Fine-tuning the LLM

This step will target the infamous "hallucinations" of large language models. 

Together with our clients we identified best practises to fine-tune the search, question generation, retrieval of data, and thereby the very result supporting your team in their daily work. 

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We are your implementation partner for RAG solutions

At Valprovia, we offer comprehensive RAG consulting and implementation services to help you harness the full potential of your data. Whether you're a small business or a large corporation, our expertise ensures we can tailor solutions to meet your unique needs.

If you're interested in exploring Retrieval-Augmented Generation projects or enhancing your current AI capabilities, we invite you to reach out to us. Our team is ready to provide personalized consultations, demos, and strategic guidance to help you achieve your goals.

Contact us today to start your RAG journey!

We are an AI development company from Germany.

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Download: Microsoft Copilot & AI Readiness Checklist

Are you ready to harness the power of AI in your business? Get ahead with our ๐—™๐—ฅ๐—˜๐—˜ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ผ๐—ฝ๐—ถ๐—น๐—ผ๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—œ ๐—ฅ๐—ฒ๐—ฎ๐—ฑ๐—ถ๐—ป๐—ฒ๐˜€๐˜€ ๐—–๐—ต๐—ฒ๐—ฐ๐—ธ๐—น๐—ถ๐˜€๐˜!

As AI continues to transform the business landscape, it's crucial to understand how prepared your organization is to adopt and integrate these cutting-edge technologies. Our checklist provides you with the insights you need to evaluate your current capabilities and readiness for AI implementation.

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Frequently asked questions:

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating them with external knowledge bases. This allows LLMs to access up-to-date information, improving the accuracy and relevance of their responses.

RAG retrieves relevant data from external sources before generating responses, enhancing content without retraining the model. It combines generative and retrieval mechanisms to deliver reliable, tailored responses, maintaining user confidence and improving the overall AI experience.

RAG improves the performance of language models by combining information retrieval with text generation. It first searches a large database for relevant data, then uses this data to generate more accurate and contextually relevant answers. This dual approach enhances the modelโ€™s ability to provide precise and informative responses, leveraging the companyโ€™s existing knowledge base.

The main components of a RAG model are the retriever and the generator. The retriever searches a large database to find relevant information based on the input query. The generator then uses this retrieved information to create a detailed and contextually accurate response. This combination ensures high-quality answers by leveraging extensive knowledge sources.

At Valprovia we put a lot of extra effort into the search component, this prepares the question for the retriever and combines keyword and free-text search for more accurate results. 

The retrieval mechanism in RAG works by searching through a large database (vector database, SharePoint, SQL Database) to find relevant information based on the input query. It uses algorithms to match keywords and context from the query with the data stored in the database. The most pertinent information is then passed to the generator, which uses it to create a precise and contextually accurate response.

The retrieval process in RAG can utilize various data sources, including internal company databases, documents, websites, SharePoint, and external knowledge bases. These sources can contain structured data, such as databases and spreadsheets, as well as unstructured data, like text documents, emails, and web pages. This flexibility allows RAG to access a wide range of information to generate accurate and relevant responses.
RAG has practical applications across various industries, including customer support, where it can provide accurate responses to inquiries, and healthcare, where it can assist in diagnosing conditions by retrieving relevant medical information. In finance, RAG can generate detailed reports by analyzing large datasets, and in education, it can offer personalized tutoring by accessing vast educational resources. These applications demonstrate RAG's versatility and effectiveness in enhancing information retrieval and response generation.
RAG handles outdated or incorrect information by leveraging regular updates and sophisticated filtering algorithms. It prioritizes more recent and reliable sources during the retrieval process, reducing the likelihood of using outdated data. Additionally, the system can be trained to recognize and discard information that doesn't meet quality standards, ensuring that the generated responses are accurate and relevant.

RAG can be integrated into existing AI systems by connecting the retriever and generator components to the system's data sources and APIs. The retriever searches for relevant information from these sources, while the generator uses this data to create accurate responses. Integration involves customizing the RAG model to align with the specific needs and workflows of the AI system, ensuring seamless interaction and enhanced performance. Regular updates and maintenance are required to keep the RAG system functioning effectively within the existing AI infrastructure.

Future prospects for RAG technology include improved retrieval algorithms for better accuracy and relevance, as well as enhanced integration with various data sources and AI systems. Advancements are expected in the areas of real-time data processing, reducing latency, and increasing the speed of response generation. Additionally, RAG technology will likely see improved handling of unstructured data and more sophisticated methods for filtering out outdated or incorrect information.

Valprovia offers comprehensive support in developing a RAG solution through our expertise and resources. We provide:

  1. AI Readiness Platform: Our platform ensures your organization is prepared to leverage RAG technology, enhancing data integration and management.
  2. Experience and Tools: With years of experience and advanced tools, we help structure and organize your data effectively.
  3. Iterative Approach: We use an iterative development process and reusable components to shorten project timelines and deliver faster results.
  4. Custom Solutions: Our team specializes in creating custom RAG solutions tailored to your specific needs and business use cases.
  5. End-to-End Support: From initial consultation and PoC development to full implementation and ongoing support, we partner with you every step of the way to ensure success.

Partner with Valprovia to confidently harness the power of RAG systems and elevate your organizationโ€™s data management and AI integration.

We take an iterative approach and you don't start from scratch with us. We bring our reusable components, which shorten the project time. You can expect a Proof of Concept (PoC) phase of approximately 1-2 months, and the results of the first release typically within 2-4 months.

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