{"id":4959,"date":"2025-03-17T14:43:24","date_gmt":"2025-03-17T17:43:24","guid":{"rendered":"https:\/\/wearebigcheese.com\/we-are-your-ideal-genai-partner\/"},"modified":"2025-03-17T16:09:19","modified_gmt":"2025-03-17T19:09:19","slug":"geaai-in-aws","status":"publish","type":"page","link":"https:\/\/wearebigcheese.com\/en\/geaai-in-aws\/","title":{"rendered":"We are your ideal GenAI partner"},"content":{"rendered":"\n
<\/div>
\n
\n
<\/div>
\n
\n

services<\/h1>\n\n\n\n

We are your ideal GenAI partner<\/h2>\n\n\n\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

In the age of artificial intelligence, reliable, secure and scalable generative solutions are key to business innovation. <\/p>\n\n\n\n

BigCheese, as a certified <\/strong> Premier<\/strong> Partner <\/strong>, offers advanced implementations based on AWS Bedrock and Retrieval-Augmented Generation (RAG)<\/strong> to transform the operation and user experience in your organization.<\/p>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
<\/div>
\n
\n
\"\"<\/a><\/figure><\/div><\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
<\/div>
\n
\n
<\/div>
\n
\n
\n
\n

AWS GenAI<\/h2>\n<\/div>\n\n\n\n
\n
<\/div>
\n
<\/div>\n\n\n\n
\n

BigCheese is an AWS Premier Partner<\/strong> with an extensive track record of successful implementations of generative artificial intelligence based solutions.<\/p>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n

\n
\n
\n
Let’s talk<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
<\/div>
\n
\n
01<\/div>\n<\/div>\n\n\n\n
\n

Advanced and scalable models<\/strong><\/h3>\n\n\n\n

Access foundational AWS Bedrock models with flexible and secure integrations.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
02<\/div>\n<\/div>\n\n\n\n
\n

Optimization of internal information<\/strong><\/h3>\n\n\n\n

Use RAG to improve the quality and relevance of the responses generated.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
03<\/div>\n<\/div>\n\n\n\n
\n

Automation and efficiency<\/strong><\/h3>\n\n\n\n

Reduces operational burden in customer service, technical support and knowledge management.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
04<\/div>\n<\/div>\n\n\n\n
\n

Regulatory compliance<\/strong><\/h3>\n\n\n\n

Security and privacy guaranteed with advanced content and access filters.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
05<\/div>\n<\/div>\n\n\n\n
\n

Frictionless integration<\/strong><\/h3>\n\n\n\n

Connects the solution with databases, APIs and enterprise systems.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
06<\/div>\n<\/div>\n\n\n\n
\n

Optimized scalability and costs<\/strong><\/strong><\/h3>\n\n\n\n

On-demand and cloud-optimized architectural design.<\/p>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
<\/div>
\n
\n

Turn artificial intelligence into your competitive advantage with BigCheese, an AWS GenAI certified partner.<\/p>\n\n\n\n

\"\"<\/figure>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
<\/div>
\n
\n
<\/div>
\n
\n
\n
\n

Lorem ipsum dolor sit amet<\/p>\n\n\n\n

BigCheese, as an AWS Premier Partner, has solid experience in the implementation of generative artificial intelligence solutions, achieving successful results for various industries.<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Certified experience<\/strong><\/h3>\n\n\n\n

BigCheese stands out for its commitment to excellence and quality, backed by several certifications that endorse its experience and professionalism in the technological field.<\/p>\n\n\n\n

\n
Certifications<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n

Proven methodology<\/strong><\/h3>\n\n\n\n

Our Think Big, Start Small<\/strong> approach ensures scalable and effective implementations. Our goal is to transcend the Proof of Concept so that the business is positively impacted by GenAI. <\/p>\n<\/div>\n\n\n\n

\n

Integration with RAG<\/strong><\/h3>\n\n\n\n

We implement advanced Retrieval-Augmented Generation<\/strong> strategies for accurate contextual responses.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Guaranteed safety<\/strong><\/h3>\n\n\n\n

We apply strict privacy and compliance controls (GDPR, CCPA, ISO 27001). Particularly ISO 27001 <\/strong>as it deals with the security of the information handled. <\/p>\n<\/div>\n\n\n\n

\n

Support and training<\/strong><\/h3>\n\n\n\n

We provide assistance to your technical team and train users for an efficient adoption of the technology.<\/p>\n<\/div>\n\n\n\n

\n

Scalability and expansion<\/strong><\/h3>\n\n\n\n

We adjust the infrastructure of the solution to adapt to the growth of the business, guaranteeing its evolution without affecting performance.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n

\n
Schedule a call<\/a><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
<\/div>
\n
\n
<\/div>
\n
\n

What is a RAG?
<\/strong>Retrieval-Augmented Generation (RAG) is an artificial intelligence technique that combines generative models with information retrieval systems to generate more accurate answers based on up-to-date data. <\/p>\n\n\n\n

\"\"<\/figure>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
<\/div>
\n
\n
<\/div>
\n
\n
\n
\n

Implementation of GenAI with BigCheese<\/h2>\n<\/div>\n\n\n\n
\n
<\/div>
\n
<\/div>\n\n\n\n
\n

BigCheese has developed a structured approach to the implementation of GenAI solutions, ensuring efficiency, security and scalability. Our process consists of four key phases: <\/p>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n

\n
\n
\n
Let’s talk<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
<\/div>
\n
\n
01<\/div>\n<\/div>\n\n\n\n
\n

Assessment: Initial evaluation and analysis<\/strong><\/h3>\n\n\n\n

In this stage we analyze the client’s data ecosystem, identify the most relevant use cases and evaluate the existing infrastructure to determine the best implementation strategy.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
02<\/div>\n<\/div>\n\n\n\n
\n

Strategy: Solution design<\/strong><\/h3>\n\n\n\n

We define the GenAI + RAG solution architecture, establish the governance, security and compliance rules, and design the AI models adapted to the client’s specific needs.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
03<\/div>\n<\/div>\n\n\n\n
\n

Implementation: Development and deployment<\/strong><\/h3>\n\n\n\n

We implemented the solution on AWS Bedrock<\/strong>, integrated the data sources and optimized system performance through continuous testing and tuning to ensure a smooth and efficient experience.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
04<\/div>\n<\/div>\n\n\n\n
\n

Post-Implementation: Monitoring and Optimization<\/strong><\/h3>\n\n\n\n

After deployment, we focus on maintenance, optimization and continuous improvement. We implement monitoring tools, perform periodic adjustments and train the client’s team for the autonomous management of the solution. <\/p>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
<\/div>
\n
\n

FAQS<\/h2>\n\n\n\n

Quick answers to your questions
<\/h3>\n<\/div>\n\n\n\n
\n
\n
1. Why is it important to have a RAG?<\/div><\/summary>
\n\n

A RAG improves the accuracy of the responses of a generative model, avoiding hallucinations and ensuring that the information used is relevant and reliable.<\/p>\n\n<\/div><\/details>\n\n\n\n

2. What do we save with a RAG?<\/div><\/summary>
\n\n

It reduces the need to train models with large volumes of data, lowers infrastructure costs and improves operational efficiency by providing answers based on structured, real-time data.<\/p>\n\n<\/div><\/details>\n\n\n\n

How is a RAG integrated with AWS Bedrock?<\/div><\/summary>
\n\n

AWS Bedrock allows the use of foundational models combined with databases and vector storage to improve the quality of responses.<\/p>\n\n<\/div><\/details>\n\n\n\n

Is it safe to use RAG in a business environment?<\/div><\/summary>
\n\n

Yes, by implementing security filters, governance and access control, we ensure regulatory compliance and data privacy.<\/p>\n\n<\/div><\/details>\n\n\n\n

5. What types of companies can benefit from RAG?<\/div><\/summary>
\n\n

Any company that handles large volumes of information, such as banks, insurance companies, ecommerce, customer service and technical support.<\/p>\n\n<\/div><\/details>\n\n\n\n

How long does it take to implement a GenAI solution with RAG?<\/div><\/summary>
\n\n

Implementation time varies depending on the scope of the project, but our structured approach allows us to have an MVP in a few weeks.<\/p>\n\n<\/div><\/details>\n\n\n\n

7. How is a RAG maintained and updated?<\/div><\/summary>
\n\n

Through MLOps strategies, updating of data sources and periodic retraining of models to maintain their accuracy.<\/p>\n\n<\/div><\/details>\n\n\n\n

What are the costs associated with a GenAI solution with RAG?<\/div><\/summary>
\n\n

It depends on the infrastructure used, but with AWS we can optimize costs and adjust the solution according to business demand.<\/p>\n\n<\/div><\/details>\n\n\n\n

How do I get started with BigCheese?<\/div><\/summary>
\n\n

Contact our team for an initial assessment and let’s design together the best solution for your company.<\/p>\n\n<\/div><\/details>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

<\/div>
\n
\n
<\/div>
\n
\n
\n
\n

Related cases<\/h3>\n<\/div>\n<\/div>\n\n\n
\"Culqi<\/a><\/div>\n
AI\/ML<\/a>AWS<\/a>DevOps<\/a>Financial Services<\/a><\/div>\n

Culqi Data Lake on AWS<\/a><\/h4>\n
Read More<\/a><\/div><\/div>\n
\"Midinero\"<\/a><\/div>\n
AWS Cloud<\/a>DevOps and SRE<\/a>Financial Services<\/a><\/div>\n

Midinero<\/a><\/h4>\n
Read More<\/a><\/div><\/div><\/div><\/div> <\/div>