Legal
From contract set to cited risk memo.
Contracts and policies in. Clause-level evidence and review-ready analysis out.
Work AI for
the work
your business
runs on.
Turn company knowledge into cited deliverables inside a controlled EU cloud.
Discuss your workflow01 / THE PROBLEMGeneric AI stops
Generic AI stops
at the answer.
Real work needs company context, source permissions, evidence, and a finished output.
Approved inputsCompany knowledgeDocuments · Messages · Systems
Verifiable outputsFinished workAnswers · Memos · Presentations
DianaWork AI engine
01 Connect→02 Understand→03 Create→04 Cite
02 / THE SYSTEMKnowledge in.
Knowledge in.
Verified work out.
Diana connects permitted sources, reasons across them, and keeps evidence attached to every result.
03 / THE OUTCOMEPowerful AI
Powerful AI
you can verify.
Every conclusion keeps its evidence. Every action stays visible.
Choose your teamDifferent teams.
Different teams.
One company context.
Finance
From scattered figures to one defensible board narrative.
Spreadsheets and filings in. Cell-linked analysis and board materials out.
IT & Security
Useful company AI without losing the boundary.
Policies, incidents, and vendor records in. Evidence packs and briefs out.
Executive
One briefing. Every claim accounted for.
Approved context across departments in. One source-linked narrative out.
01 / 04
Power you can verifyAI is only powerful
AI is only powerful
when the work holds up.
Search across approved files, messages, databases, and long-lived systems.
Compare evidence across sources before reaching a conclusion.
Link material claims to the file, page, clause, or cell.
Produce review-ready Word, PowerPoint, PDF, and Excel documents.
Keep approvals and activity in one Audit Stream.
One workflow,
end to end
end to end
Bring together documents, messages, databases, case tools, and proprietary systems without changing source permissions.01 Connect — Approved sources
Real value, visible in the workPowerful AI should handle complexity.
From difficult question to finished output.
Powerful AI should handle complexity.
Great Work AI should make it usable.
Give Diana fragmented sources, a difficult question, and a real deliverable. Review the result, the evidence, and every action behind it.
Start with one workflowProve the work.
Prove the work.
Then expand.
Scope
Choose one valuable, repeatable workflow and agree on its current baseline.
Connect
Confirm sources, permissions, and the connectors required for the work.
Deploy
Select dedicated EU cloud, on-premise, or air-gapped architecture.
Validate
Test output quality, citations, approvals, and Audit Stream evidence.
Expand
Reuse approved context and successful workflows across adjacent teams.
Regulated-industry overlaysSame Work AI.
Same Work AI.
Different constraints.
Diana adapts the workflow, evidence, and deployment to the work your industry requires.
EU deployment Controlled
Sources→Context→Work→Audit
The commercial advantage
EU cloud is only the beginning.
Processing, permissions, evidence, and audit stay inside one controlled deployment.
Dedicated EU cloud
Keep company context inside a controlled European deployment by default.
Learn moreNo external LLM APIs
Run the work without sending company context to public model endpoints.
Learn moreEvidence by default
Preserve permissions, citations, approvals, and a visible Audit Stream.
Learn moreBefore the pilotQuestions about
Questions about
your workflow.
01Which workflow should we start with?
Start with repeatable, evidence-heavy work that has a clear owner, known inputs, and a reviewable output. Contract review, variance analysis, incident briefs, and board reporting are strong examples.
02How is Diana tailored to our way of working?
The workflow is configured around your approved sources, required output, review steps, and definition of acceptable work—not a generic prompt library.
03Do we need to move our knowledge into Diana?
No. Diana connects to approved systems and preserves source permissions so teams can work across company knowledge without a forced migration.
04Where does human approval happen?
Approval points are set around the risk of the workflow. Reviewers can inspect sources, edit the output, approve the next step, or stop the work before delivery.
05How do we measure a pilot?
Agree on the baseline first, then measure review time, output quality, citation coverage, team adoption, and reduction in unmanaged AI use.
06How does one workflow expand to other teams?
Approved connections, permissions, and reusable skills become a foundation for adjacent workflows, with each expansion validated against its own owner and success criteria.
