Today’s challenge
The adoption of AI by enterprises has surged and is expected to continue accelerating at an unprecedented rate. However, off-the-shelf AI solutions often lack the specificity and precision required for specialized tasks. These solutions are usually very costly. Traditional approaches to addressing these challenges include Retrieval-Augmented Generation (RAG), fine-tuning, and alignment.
Each approach has its trade-offs. RAG offers a simpler and quicker setup but may not achieve the same accuracy as fine-tuning. Fine-tuning, while more complex and resource-intensive, can yield significantly higher accuracy and performance for specialized tasks. However, privacy is a key factor to consider, especially with fine-tuning, where sensitive data may be involved in adjusting the model.
Our solution
Our solution combines advanced Hybrid RAG techniques, with fine-tuning and alignment to deliver precise, contextually aware responses. We prioritize data anonymization to ensure all sensitive information remains GDPR-compliant, allowing safe and compliant use of data across applications.
Each enterprise model goes through continuous self-refinement and adapts; learning from past interactions to improve accuracy over time, fostering a self-improving AI system. To enable this, we implement robust data munging and cleaning processes that unify diverse datasets into a cohesive data layer, ensuring consistent and high-quality data input.
Our optimized kernels accelerate fine-tuning, achieving high performance and reduced latency, enabling rapid model adaptation. Finally, our flexible infrastructure supports seamless inferencing for fine-tuned AI models, offering reliable and scalable management for real-time deployments.
Key technology enablers
Data anonymization
Data anonymization involves transforming sensitive information to prevent the identification of individuals in datasets. Techniques such as data masking, generalization, or pseudonymization are used to protect privacy, making it possible to work with datasets for analysis, training, or fine-tuning without exposing personal or sensitive information.
GenAI (Generative AI)
GenAI refers to models that generate new data, such as text, images, or code, based on learned patterns. Large language models (LLMs) are a prominent example, used in applications from chatbots to content creation. GenAI models leverage extensive datasets to create highly human-like outputs, enabling various applications like summarization, translation, and creative writing.
Fine-tuning
Fine-tuning customizes pre-trained models on specific datasets to enhance performance in a particular domain or for a specific task. It improves the model’s relevance and accuracy by adjusting weights based on new, specialized data, aligning the model’s output closer to the desired results.
Alignment
Alignment ensures AI systems adhere to user intentions, values, and ethical standards. In the context of LLMs, alignment involves training techniques or control mechanisms to make models behave as expected, providing safe and reliable outputs. This is crucial in high-stakes applications where accuracy and ethical concerns are vital.
RAG (Retrieval-Augmented Generation)
RAG combines traditional retrieval techniques with generative AI models to enhance responses by drawing relevant information from a database or knowledge base. This approach improves answer accuracy and relevance, especially for queries requiring current or domain-specific knowledge that the generative model alone might lack.
Knowledge graphs
Knowledge graphs organize information in a structured, interconnected format, often with entities (like people, places, or concepts) as nodes and relationships as edges. They provide context and help models understand relationships within data, making it easier to retrieve and reason with complex information. This is essential for applications requiring comprehensive, factual responses.
Inferencing
Inferencing is the process of running trained models on new data to generate predictions or insights. This involves feeding real-world data through a model to get results, such as classifications, translations, or decision-making. Effective inferencing requires optimized model deployment to ensure low-latency, high-accuracy outputs for applications ranging from real-time recommendations to complex analytics.
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