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We are your ideal GenAI partner
In the age of artificial intelligence, reliable, secure and scalable generative solutions are key to business innovation.
BigCheese, as a certified Premier Partner , offers advanced implementations based on AWS Bedrock and Retrieval-Augmented Generation (RAG) to transform the operation and user experience in your organization.
AWS GenAI
BigCheese is an AWS Premier Partner with an extensive track record of successful implementations of generative artificial intelligence based solutions.
Advanced and scalable models
Access foundational AWS Bedrock models with flexible and secure integrations.
Optimization of internal information
Use RAG to improve the quality and relevance of the responses generated.
Automation and efficiency
Reduces operational burden in customer service, technical support and knowledge management.
Regulatory compliance
Security and privacy guaranteed with advanced content and access filters.
Frictionless integration
Connects the solution with databases, APIs and enterprise systems.
Optimized scalability and costs
On-demand and cloud-optimized architectural design.
Turn artificial intelligence into your competitive advantage with BigCheese, an AWS GenAI certified partner.
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BigCheese, as an AWS Premier Partner, has solid experience in the implementation of generative artificial intelligence solutions, achieving successful results for various industries.
Certified experience
BigCheese stands out for its commitment to excellence and quality, backed by several certifications that endorse its experience and professionalism in the technological field.
Proven methodology
Our Think Big, Start Small approach ensures scalable and effective implementations. Our goal is to transcend the Proof of Concept so that the business is positively impacted by GenAI.
Integration with RAG
We implement advanced Retrieval-Augmented Generation strategies for accurate contextual responses.
Guaranteed safety
We apply strict privacy and compliance controls (GDPR, CCPA, ISO 27001). Particularly ISO 27001 as it deals with the security of the information handled.
Support and training
We provide assistance to your technical team and train users for an efficient adoption of the technology.
Scalability and expansion
We adjust the infrastructure of the solution to adapt to the growth of the business, guaranteeing its evolution without affecting performance.
What is a RAG?
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.
Implementation of GenAI with BigCheese
BigCheese has developed a structured approach to the implementation of GenAI solutions, ensuring efficiency, security and scalability. Our process consists of four key phases:
Assessment: Initial evaluation and analysis
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.
Strategy: Solution design
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.
Implementation: Development and deployment
We implemented the solution on AWS Bedrock, integrated the data sources and optimized system performance through continuous testing and tuning to ensure a smooth and efficient experience.
Post-Implementation: Monitoring and Optimization
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.
FAQS
Quick answers to your questions
1. Why is it important to have a RAG?
A RAG improves the accuracy of the responses of a generative model, avoiding hallucinations and ensuring that the information used is relevant and reliable.
2. What do we save with a RAG?
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.
How is a RAG integrated with AWS Bedrock?
AWS Bedrock allows the use of foundational models combined with databases and vector storage to improve the quality of responses.
Is it safe to use RAG in a business environment?
Yes, by implementing security filters, governance and access control, we ensure regulatory compliance and data privacy.
5. What types of companies can benefit from RAG?
Any company that handles large volumes of information, such as banks, insurance companies, ecommerce, customer service and technical support.
How long does it take to implement a GenAI solution with RAG?
Implementation time varies depending on the scope of the project, but our structured approach allows us to have an MVP in a few weeks.
7. How is a RAG maintained and updated?
Through MLOps strategies, updating of data sources and periodic retraining of models to maintain their accuracy.
What are the costs associated with a GenAI solution with RAG?
It depends on the infrastructure used, but with AWS we can optimize costs and adjust the solution according to business demand.
How do I get started with BigCheese?
Contact our team for an initial assessment and let’s design together the best solution for your company.
Customers
They trust us
