Worthwhile Chat has been designed with privacy and security as core principles. User conversations are transmitted over encrypted connections and processed within enterprise AWS infrastructure using Amazon Bedrock. Data is encrypted both in transit and at rest, and access is restricted through role-based access controls, audit logging, and organisational security policies.

Where appropriate, automated safeguards are used to detect and redact personally identifiable information (PII) before it is processed by AI models. We also have Data Processing Agreements (DPAs) with our AI providers that prohibit them from using your prompts or responses to train their own models.

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No. Your prompts, uploaded documents, and conversations are not used to train foundation models.

Worthwhile Chat accesses AI models through enterprise API agreements via Amazon Bedrock. Under these agreements, prompts and responses are processed solely to provide the requested service and are not retained for model training by the model providers.

We may analyse anonymised and aggregated usage trends (for example, the types of tasks charities are performing) to improve Worthwhile Chat itself, such as refining prompts, improving features, and developing new charity-specific functionality. This analysis does not involve training the underlying AI models.

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User data is processed within AWS-hosted infrastructure using approved UK/EU regions where available. Conversation data may be retained for operational purposes such as maintaining chat history, auditing, troubleshooting, security monitoring, and service improvement, in accordance with our retention policies.

Access to stored data is limited to authorised personnel with a legitimate operational need and is protected through technical and organisational security controls.

Worthwhile Chat has been designed to support organisations in meeting UK GDPR requirements. Our legal basis for processing includes Contractual Necessity (to provide the service) and Legitimate Interest where appropriate.

We maintain Data Processing Agreements with our AI providers, implement encryption in transit and at rest, apply access controls and audit logging, and use enterprise API agreements that prevent providers from using customer data for model training.

Organisations remain responsible for ensuring their own use of the platform complies with applicable data protection legislation and internal policies.

Yes, but users should continue to follow their organisation's data protection policies and only upload information that is appropriate for the task.

Worthwhile Chat includes technical safeguards designed to protect sensitive information, including encrypted transmission, secure enterprise processing, and automated PII detection where appropriate. However, no automated system can guarantee the identification or removal of all sensitive information, so users should continue to exercise professional judgement when handling confidential data.

Yes, just email us at worthwhile@wearecast.org.uk and request to have your data deleted. We will get your data deleted as soon as we can and let you know once this is done.

Worthwhile Chat works like most general purpose, generative AI tools. You type instructions into the prompt input area, and Worthwhile Chat will return a response. See our Help & Support page for more guidance on using Worthwhile Chat.

Worthwhile Chat works like most general purpose, generative AI tools. You type instructions into the prompt input area, and Worthwhile Chat will return a response. See our Help & Support page for more guidance on using Worthwhile Chat.

Worthwhile Chat is built on a secure AI platform using Open WebUI and LiteLLM, with foundation models accessed through Amazon Bedrock.

Depending on the task, requests may be routed to different enterprise-approved models, such as Anthropic Claude or other supported models. Users do not communicate directly with public AI services; all model access takes place through managed enterprise infrastructure designed to provide stronger privacy, governance, and security controls.

As models evolve, we may update the models available through the platform while maintaining the same privacy and contractual safeguards.

The models available for selection are Claude Haiku 4.5 and Claude Sonnet 4.5. Haiku 4.5 is good for quick and efficient responses. Choose this model for everyday questions, drafting, and simple tasks. Sonnet 4.5 is a more capable model. Choose this model for complex writing, reasoning and planning. We encourage a level of experimentation with both models, so that people can see for themselves how well they perform on certain tasks.

You can find more information about Haiku 4.5 and Sonnet 4.5 on the Anthropic website.

Claude Haiku 4.5 (in Amazon Bedrock) has a training data cutoff date of February 2025. See this Claude Haiku 4.5 guide on the Amazon Bedrock website.

Claude Sonnet 4.5 (in Amazon Bedrock) has a training data cutoff date of April 2025. See this Claude Sonnet 4.5 guide on the Amazon Bedrock website.

Folders allow you to group related conversations. Create folders and name them to suit your needs, for example by project or theme. When creating a folder, you can also add a system prompt, knowledge, and files that will apply to all chats within that folder.

You can drag and drop chats into folders. Edit folder settings by clicking the three-dot menu (⋯) to the right of the folder name.

Prompts allow you to quickly access in-built, pre-written instructions to use within Worthwhile Chat. These prompts are a starting point to help you use AI to perform common charity tasks. We encourage you to explore these prompts, try them out, and adapt them for your specific context and needs. The in-built prompts in Worthwhile Chat mirror this list of Use Cases.

Yes, Worthwhile Chat automatically stores past chats. You’ll see past chats in the left side bar, where you can organise them into folders, and also share, download, rename, pin, clone, archive and delete them.

However, if you don’t want a chat to be saved, you can click the “Temporary Chat” icon top right of the screen before you start typing in your prompt.

It’s unlikely that Worthwhile Chat will offer this functionality. However, we are working out how integrations will be handled in Worthwhile Teams, where we hope to offer organisations the ability to connect to existing systems and knowledge stores.

We take a multi-layered approach to safety and bias. This includes using technical safeguards to reduce harmful outputs, and ongoing monitoring to help identify and address potential issues. We recognise that no AI system is entirely free from bias, and we encourage users to review outputs critically and report any concerns.

All Large Language Models require significant computational power. However, by using Amazon Bedrock, we are leveraging some of the world’s most energy-optimised data centers. Using a shared, managed infrastructure is generally more carbon-efficient than organisations running their own hardware. We continue to monitor the efficiency of the models we select to ensure we are balancing technical power with environmental responsibility.

Worthwhile Chat is built with a "Privacy and Inclusion First" mindset. We aim for compliance with WCAG 2.1/2.2 AA standards to ensure the tool is usable by people with a wide range of access needs, including screen-reader compatibility and high-contrast modes.

We’re actively working on an accessibility audit and accessibility statement.

Yes. Because the underlying models are multilingual, Worthwhile Chat can understand and respond in many languages.

Both CAST and The Developer Society are nonprofits. Worthwhile Chat is built on a sustainable 'Social Enterprise' model, where any usage fees go directly back into the tool’s development and maintenance. We are building this as a long-term piece of sector infrastructure, designed to be resilient and independent of short-term grant cycles.

As non-profits, any surplus generated by paid access to Worthwhile Chat is legally required to be reinvested into our mission. 100% of the surplus goes back into developing the tool, improving safety features, and subsidising access for smaller organisations.