watsonx.ai - The trusted cloud

Securing your data

Built on Red Hat OpenShift, IBM's watsonx.ai platform enables the training, enrichment, use and governance of large language models (LLMs), within a controlled and managed framework within Cloud Temple's SecNumCloud environment.

Combining performance and flexibility

Take full advantage of the advanced capabilities of watsonx.ai and its vast catalogue of open-source and commercial models on Cloud Temple's powerful and flexible infrastructure, which guarantees very high availability and optimised deployments for all your AI projects.

Increasing the benefits of AI

Optimise models with minimal data, thanks to advanced prompt optimisation features, a comprehensive SDK and API libraries. Automate large-scale processes and leverage custom templates to reduce operational costs and improve efficiency.

Optimising governance

Implement end-to-end AI governance, aligning business strategy with technology and business outcomes. Accelerate the impact of AI with trusted data across your organisation, without compromising on data security.

Multi-model catalogue
Wide choice of IBM-developed models such as Granite, open source and third-party models such as Llama 3 and Mixtral 8x7b, or Bring Your Own Model (BYOM) option via Hugging Face

Multi-cloud deployment
Flexibility to deploy your AI solutions and workloads according to their criticality in the clouds of your choice, including Cloud Temple's trusted cloud

Prompt lab
Creation of prompts from foundation models or using prompt engineering, and interaction with models in Chat, Freeform or Structured mode

Finetuning
Optimisation of foundation model prompts using labelled data to adapt models to new tasks without having to re-train them

MLOps
Automation of the AI model lifecycle thanks to numerous API, SDK and library connections, as well as tooling, pipelines and execution environments powered by foundation models

Pricing

Use cases

Creating and sharing resources for your organisation

Develop a Question/Answer resource from a vast internal or external knowledge base. Use generative AI to analyse numerous documents to provide relevant answers, enriched by real-time information feeds, and improve your organisation's documentation.

Use cases

Trend detection

Use large amounts of data to identify key information from documents, customer interactions or IT incidents. Using generative AI, discover patterns and anomalies from which traditional AI and ML models can then make predictions and forecasts.

Use cases

Management of sensitive data

Create synthetic tabular data to protect sensitive information during test phases. Use data generated by simulation or algorithms to fill gaps and reduce the risk of exposure of personal data.

Computer and robot representing generative AI

Use cases

Content production

Create technology, content and even code to improve your organisation's productivity. Content generated can include programme development, ideas for marketing campaigns, technical documentation, user profile development, automated reports, scripts and much more.