Compound AI Systems (AICS) and AI Agents (AIA) in Advanced Analytics
Currently, in the business context, data is one of the most valuable resources that organizations have. They enable companies to make informed decisions related to their business, which highlights the crucial importance of data analytics and science in our daily lives.
However, companies that want to generate value from large amounts of data face a set of challenges:
- Ensure the quality, integrity and consistency of data;
- complexity of integrating data from different sources and with different formats;
- contextualization and enrichment, which are decisive;
- security and protection against breaches;
- management of large volumes of data without jeopardizing its performance;
- implementation of effective governance policies;
- and the lack of specific in-house technical know-how to manage, analyze, and generate actionable insights from this information.
To address these challenges, organizations must adopt a strategic approach, taking advantage of advanced technologies and best practices in data management.
AI Compound Systems (AICS) and AI Agents (AIA) are two essential technologies in this domain, automating data processing, improving data quality, and providing critical information in real-time.
AIAs are designed to perform tasks autonomously
The AI Compound Systems (AICS) are advanced architectures that integrate various AI models and tools to solve complex problems more effectively than Single Model approaches. These systems combine various components, such as Natural Language Processing (NLP), Machine Learning (ML), and external data sources.
AIAs are designed to perform tasks autonomously, interacting with their environment, making decisions, and executing actions to achieve specific goals.
These systems can increase the performance of predictive systems, improve data reliability, support decision-making, and promote a data-driven corporate culture, leading to financial benefits.
However, the design and maintenance of these technologies are complex due to the need to integrate multiple components, which require in-depth technical knowledge of AI sub-disciplines. In addition, the development and improvement of these systems requires significant intensive resources, computing skills and expertise.
There are solutions available on the market such as RASA and Stratio that use a concerted strategic approach of AICS and EIA combining them with business logic, semantic business layers and flow retrieval.
Izertis has significant experience in the development and adaptation of AI technologies
Rasa is an AI platform that uses Natural Language Understanding (NLU) through built-in ML techniques and user dialogue management, enabling developers to create sophisticated and intuitive conversational intelligent applications.
Stratio is a data fabric with different AI technologies to improve data quality, security, and business context, which uses semantic ontology to provide context, meaning, and unify data from multiple sources, and achieve more accurate predictions and decisions, integrated with the most effective LLMs (Gemini, OpenAi, Met Llama 3, and Claude 3) on the market.
Izertis has relevant experience in the development and adaptation of AI technologies to the needs of your company, through the partnership with RASA and Stratio can help your organization in this digital transformation process.