Give your team leading AI models that use tokens instead of detected personal data. Compare enterprise workspaces, costs, residency and tasks.
Employees should be able to use frontier AI for useful drafts and analysis without sending protected original personal values to the model provider. A business workspace purchase should also resolve who approves sensitive work and what the organisation pays for. Evaluate Sluis Workspace against those decisions before comparing product labels.
Start with employee work and information boundaries
Sluis Workspace is a managed, EU-first AI workspace. With tokenization, detected protected personal values become typed, reversible tokens before a request reaches the model provider. The provider works with those tokens, not the protected originals. Sluis restores the values on the return path; the token mapping is never sent to the provider. Your team receives a useful answer with relevant names or other protected values restored.
Illustrative scenario: an HR team uses an approved workflow to draft an employee meeting invitation. Test detection and the restored result before rollout. Missed identifiers, residual context and unsupported content can still expose personal information; allow_log only records detections and does not protect the text. Optional hosted inspection receives remaining eligible text after local protection. Review that recipient, retention and provider terms separately. Managed Sluis is not infrastructure you operate yourself.
Define which tasks and information are permitted. Treat company knowledge search, SharePoint and Google Drive access as requirements to verify, not assumed connectors.
- Choose representative tasks with measurable acceptance criteria.
- Classify information allowed in each task.
- Verify source access and permissions for proposed connectors.
Compare alternatives using one evaluation brief
ChatGPT, Microsoft 365 Copilot and Langdock can enter a shortlist as candidate suppliers. Validate current primary sources before assigning capabilities, residency commitments or commercial terms to any of them.
Use the existing European alternatives and Langdock comparison pages to frame questions. Require the same task demonstrations and data-flow evidence from each candidate rather than declaring a universal winner.
- Score candidates against the same mandatory requirements.
- Date and retain supplier evidence for each claim.
- Test output suitability with accountable human reviewers.
Evaluate usage costs and evidence together
Sluis pricing is usage-based. Model realistic workloads and consult /pricing; usage-based billing does not by itself establish contract duration, licence conditions or the absence of other commercial obligations.
Sluis publishes ISO 27001 information. Review the published scope alongside data-protection terms, content retention and deletion procedures. Certification is procurement evidence, not a guarantee that your workflow meets every legal requirement.
- Estimate usage for light and intensive employee workflows.
- Confirm contract, licence and support terms in writing.
- Review certification scope and retention settings.
Make sector requirements explicit before approval
Municipalities and public bodies may need records controls; legal and accounting teams may prioritise professional confidentiality. HR, healthcare and education workflows need their own assessment of sensitive data and human oversight.
Treat these as requirements to validate, not proof of sector suitability. A workflow that drafts public guidance has a different risk profile from one interpreting patient records or advising on an individual legal case.
- Assign a process owner and privacy reviewer.
- Define prohibited inputs and decisions requiring human approval.
- Approve a bounded workflow before expanding access.
Compare workspaces on the same employee tasks
Add Dust and GreenPT to a shortlist only when their current scope fits your requirements. Compare actual document access, permitted models, administration and commercial terms with Langdock, Microsoft 365 Copilot, ChatGPT Enterprise, Claude for Work, Gemini and Mistral Le Chat. Check the primary supplier pages and the existing Sluis comparisons; do not infer feature parity from an alternative-product label.
Questions before you decide
Which business AI is GDPR-proof?
No product label guarantees compliance. Assess your purposes, data, contracts, configuration and working practices with the appropriate legal and privacy reviewers.
Does EU-first mean all workspace data stays in Europe?
Do not infer that from positioning alone. Verify the selected models, processing routes, retention and contractual commitments in the residency documentation.
Should we replace ChatGPT or Copilot immediately?
Start with a bounded evaluation. Compare actual tasks, transition work, user guidance and current commercial terms before deciding whether replacement or coexistence is appropriate.