How each AI provider handles content processed through ResearchArk.
| Provider | Training | Retention | DPA / Terms |
|---|---|---|---|
| Anthropic | Never used for training | 30 days | anthropic.com/legal/commercial-terms |
| OpenAI | Never used for training | 30 days | openai.com/policies/business-terms |
| Google Gemini (paid) | Never used for training | 55 days | cloud.google.com/terms |
How your content is used with AI on ResearchArk, and what we never do with it
This page explains how content you produce on ResearchArk interacts with the AI features in the platform — what we do with it, what we don't, and where it is processed. It complements our Privacy Policy and GDPR Compliance pages.
When you use ArkAssist or any other AI feature, the request is sent to a language model. The model returns an answer. Once the answer is returned, the request is not added to any training dataset. We do not aggregate user prompts, drafts, or uploads into a corpus that is then used to fine-tune any model — neither models we operate ourselves nor models operated by third-party providers.
We hold contractual commitments from each model provider that user content sent through their API is not used by them for training either. Those commitments are part of the data-processing agreements in place with each provider and are reviewed periodically.
ArkAssist may route a request to one of the following providers depending on the model selected and provider availability:
Each provider is bound by a data-processing agreement that prohibits training on customer content. We may add or remove providers; this page will reflect the current set.
We log operational metadata for each AI request — provider, model, response status, latency, request identifier — so we can debug failures and detect abuse. Logged metadata does not include the prompt body or the model output. Operational logs are retained for 30 days and then deleted.
We also retain the drafts, notes, and outputs you explicitly save in your account. You can delete them at any time from the relevant section of the platform.
If your profile visibility is set to "public" or "connections only", your profile text is embedded so other users can discover you through search and matching. Embeddings are computed on our infrastructure (EmbeddingGemma-300M, run locally) and stored on ResearchArk infrastructure. Profiles with visibility set to "private" are never embedded and are excluded from search.
When your visibility is "connections only", your profile only appears in another researcher's search results if you are already connected to that researcher.
We will update this page when our practices change — for example if we add a new model provider or change a data-processing region. Material changes are announced via the platform notification system. The current version and last-updated date are shown at the bottom of this page.
Questions about this policy can be sent to privacy@researchark.eu.
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| Operation | Where it runs |
|---|
| Embedding (vectorisation) of profiles, opportunities, and search queries | On ResearchArk infrastructure, EU-region. Uses the EmbeddingGemma-300M model running locally — no external API call. |
| ArkAssist drafting, summarisation, and Q&A | Via contracted model providers. Active providers are listed below. |
| Search ranking and result aggregation | On ResearchArk infrastructure, EU-region. |
| Storage of drafts, notes, and uploads | On ResearchArk infrastructure, EU-region. |