Our approach
We’re here to help
From training data to deployment, developing generative AI tools requires expertise in machine learning models, foundation models, and computational efficiency. We help businesses explore the potential of large language models (LLMs) and synthetic data, creating AI-driven solutions that are effective, adaptable, and aligned with industry best practices.
Case studies
Case Study
A Global Reinsurance Company
A forward-looking Generative AI strategy and roadmap to unlock enterprise value and modernize ways of working.
use cases identified
transformational focus areas defined
Case Study
A Leading Financial Services Provider
A tailored GenAI solution enabled rapid, automated campaign creation.
faster content production
user relevance achieved through AI segmentation
Case Study
A Leading Pharmacy Services Provider
A purpose-built AI solution helped streamline prescription translation and unlock operational savings.
in annual savings
FAQs
Yes, fine-tuning allows us to adapt machine learning models to industry-specific needs. By training on targeted data sources, we help refine learned models for improved accuracy and relevance.
We refine text-generating and code-generated outputs through careful training, testing, and validation. By using high-quality training data and adjusting neural networks, we improve accuracy, consistency, and reliability.
We use synthetic data to fill gaps where real-world training data is limited, helping to improve model performance and adaptability. The right mix of real and synthetic data ensures AI models learn effectively and generate meaningful results.
We work with both open-source and proprietary generative AI tools, depending on your goals. Open-source models offer flexibility and innovation, while proprietary solutions provide more control and security. We can help you decide what’s best for your needs.
Contact us
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Who we are
Explore how our culture and expertise fuel digital innovation.