We help organizations identify use cases and best opportunities for deploying AI applications or machine learning models to enhance their key value drivers. A major focus is to define the data strategy and explore avenues for leveraging enterprise data.

For executives, we build roadmaps and evaluate cost, present pros and cons of commercial vs. build options.  We also highlight risks inherent in AI deployments and provide risk mitigation strategies as AI tools and technologies are evolving rapidly. For AI-driven startups, we can help with business strategy and GTM plans for products and services.

From domain-specific co-pilots to customer chatbots, enterprise-wide information retrieval systems and RAG-based LLM applications, organizations today have an array of choices for developing and deploying Gen AI applications.

A single LLM is often not sufficient to meet business requirements and today's most advanced Gen AI applications are built around compound systems.

One of the most impactful aspects of AI is in automation. We will help your organization navigate toward the development of AI capabilities and data platforms for increasingly higher levels of automation and productivity. This might include techniques for processing your enterprise's unstructured data and using AI/ML approaches to label data for training models is key to performance in industry-specific use cases.

We offer services to fine-tune foundation models or design and build industry- or domain-specific large language models (LLMs) and adapt them to specific tasks (for example, Q&A, information retrieval, image captioning, classification, instruction following). These models can be trained using your enterprise data and other information sources.

We employ the Gen AI training stack- RLHF for alignment, supervised learning with data and self-supervised learning. We employ a family of prompt engineering techniques to set your organization up with a prompt library to get the most out of your use case scenarios.