Artificial Intelligence
Everyone it seems is on an Artificial Intelligence (AI) journey to improve business efficiency, financial performance, customer and business outcomes. For AI to work, the data used to train the models needs to be fit for purpose, ensuring your AI provides reliable answers to your questions.
Governance, control and Retrieval-Augmented Generation (RAG) AI
The process of making company data fit for the purpose of training AI models is a multistep journey involving data collection, cleaning, transformation, and preparation for model
training. Ensuring the data is of high quality, well-structured, labelled (if necessary), and
privacy-compliant is critical for successful model performance and deployment. Additionally, it is necessary to ensure there is transparency as to what data has been used
to train the AI model from a governance and oversight perspective.
Infoboss provides the ideal platform for curating data ahead of submission to your AI
models for training purposes. Furthermore, when used in conjunction with our AI RAG extensions your AI models can be built, tested and undergo prompt engineering prior to adoption within your business for their intended use-case such as knowledge management, training, text generation, customer support, employee engagement, research and more.
“By 2028, 80% of business generative AI applications implemented by a RAG approach will use organisations’ existing data management platforms as the knowledge source, increasing from less than 20% today.”
Gartner
How we can help
Infoboss aids in AI governance and Retrieval-Augmented Generation (RAG) through several key features within our data management platform:
- Data Governance: Infoboss provides policies and procedures to manage data availability, usability, integrity, and security. This ensures that data used for AI is consistent, compliant with regulations, and accessible to the right stakeholders.
- Data Lineage and Traceability: Infoboss offers clear lineage tracking, making it easier to trace the origin of data used in AI training and validation. The Data Wiki and AI Corpus Training features allow for interactive exploration of data lineage from original source to model.
- Data Quality and Preprocessing: Infoboss’ data quality management helps identify and clean data issues, ensuring high-quality input for AI models.
- Transparency: AI Corpus Training tracks what data is used to train AI models, providing complete transparency about data sources used in model responses.
- RAG Architecture Support: Infoboss supports a retrieval-augmented generation architecture, where company-specific data is indexed into a vector database. This allows the AI to retrieve relevant information before generating responses, enhancing the accuracy and relevance of AI outputs.
- Custom AI Models: Infoboss enables organisations to build custom AI models trained on their own data, with complete governance and control over the training data and model access.
- Compliance: By implementing data governance measures, Infoboss’ data compliance management helps ensure AI systems comply with regulations like GDPR or CCPA.
These features allow organisations to maintain governance, control, transparency, and compliance in their AI initiatives while leveraging their own data effectively.
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