GenAI and Machine Learning

VividCloud will identify, collaboratively with your team, the technology stack and architectures that fit the GenAI and ML initiatives you are undertaking.  Data security is a key consideration from the very beginning.  A development and implementation plan is also completed at this stage.

Delivering proof of value to your stakeholders can be a very important step in the process.  We will develop a PoC that captures the essence of the proposed solution in a way that demonstrates its value.

Customer service is a key aspect of many businesses.  Coupling an LLM (e.g. ChatGPT, Claude, Stability Diffusion, etc.) with domain-specific and company-specific data through a RAG architecture can enable very advanced and effective customer service and support solutions, specific to your domain.  We have the experience and expertise to develop these solutions for you. 

Processing and preparation of disparate data and integration, and linking of data sources through an ETL or ELT solution are key aspects of any AI or ML system.  Building data infrastructures through AWS and other technologies is a key capability of VividCloud.

Retrieval-augmented generation (RAG) is a fast-moving and very beneficial technology area to leverage the promise of GenAI in the areas of large language models (LLM), images, video and data processing.  These solutions have been used in many aspects of business, including customer service and support, sales and marketing, internal operations, product development and production.  Basically, any area of a business that has large amounts of documents and data can be organized and leveraged through AI, while keeping your data private.  We have built many RAG-based systems. 

VividCloud’s core expertise is in building custom solutions that are production ready.  That includes production-ready GenAI and ML solutions that allow your company to leverage the massive benefits of these rapidly evolving new technologies.  We can integrate these solutions into your business processes and enterprise applications and move them through into production. 

ML Ops is an emerging set of capabilities that aim to deploy and maintain GenAI and ML models in production reliably and efficiently.  It is a set of practices, processes, and platforms that streamline the development and deployment of trained models (FM and ML) via monitoring, validation, and governance.