Phase 01
Route the analytic task
Choose the model path that fits the job, whether that is summarisation, entity extraction, anomaly review, or a higher-cost deep analysis pass.
Right model lane selected
Product
AI intelligence for analysts who still need to defend every conclusion
Route work across multiple models, preserve provenance, and keep human review in the loop so the agency gets speed without turning the case into an unexplainable black box.
Operational readout
routing across specialist and premium models
Multi-model
to produce a first analytic pass
<30 sec
kept inside the operational workflow
Human review
without locking the agency to one vendor lane
Provider optionality
Why it matters
Generic AI tooling fails at the exact point a case needs discipline
Most AI product pages promise speed while skipping the harder questions: which model produced the output, what evidence it touched, what the analyst changed, and how the conclusion survives legal or supervisory review.
Analysts need model output to stay attached to the case, not hidden in a disconnected chatbot transcript.
Supervisors need to see what was generated automatically, what was edited by a human, and what still needs verification.
Procurement teams need provider optionality and governance controls instead of betting the workflow on a single model contract.
Built for investigative analysis, intelligence support, and supervised AI-assisted review.
Analyst workspace
Analysts can run AI-assisted review in the same workspace where they manage entities, evidence, and case decisions.
Review cycle
Route, validate, and publish without losing provenance
The product story now follows an analytic review loop that agencies can actually govern: assign the task, run the right model path, and publish the result with analyst judgement intact.
Task intake
The intake queue gives teams a place to decide what deserves AI assistance before generated output enters the case record.
Phase 02
Review the machine output in context
Keep generated summaries, extracted entities, and supporting evidence tied to the live case so analysts can correct, merge, or reject results immediately.
Human-reviewed output ready
Phase 03
Publish with provenance
Move the reviewed result into a briefing, case note, or task package without stripping away the model path, analyst edits, or unresolved issues.
Governed analytic package published
Governed capability set
The AI page now reads like an operating brief, not a model hype page
The strongest product story is not that AI exists. It is that the agency can control how it is used, reviewed, and defended.
Routing
Task-based model selection
Different analytic jobs can use different model paths without forcing the agency into a single-provider workflow.
Premium models can be reserved for high-stakes analysis.
Routine tasks can use lower-cost routes without changing the operator experience.
The workflow stays stable even if providers change.
Review
Human-in-the-loop correction
Generated output is reviewed where the analyst already works, not in a detached chat window.
Analysts can edit, reject, or expand AI output in the case context.
Entity and narrative changes stay visible for supervisors.
Machine output does not bypass normal review discipline.
Provenance
Explainable output trail
The output package can preserve which model path was used and what changed before publication.
Supervisors can inspect the reasoning chain around the final package.
Generated content remains anchored to evidence and analyst judgement.
The AI step becomes part of the audit story instead of an invisible shortcut.
Governance
Operational controls, not just prompts
Role-based access, approvals, and publication discipline matter more than prompt engineering when the output affects a real investigation.
Governance sits inside the workflow instead of around it.
Teams can set boundaries on what gets automated and what requires review.
The same product can fit regulated, sovereign, and hybrid deployments.
Proof dossier
Specific enough for agencies evaluating real AI risk
The page now anchors on governance, deployment, and review discipline instead of interchangeable AI-platform claims.
Governance
Control posture
Human review is presented as part of the product design, not as a generic disclaimer.
Provider optionality and role-based control are treated as procurement concerns, not implementation details.
Published outputs are framed around provenance and analyst accountability.
Deployment
Deployment and provider flexibility
Cloud, hybrid, and restricted-environment deployments remain compatible with the same operator workflow.
The product narrative assumes agencies may need to swap or combine providers over time.
Model usage is described as a lane within the case workflow, not as a separate AI application.
Operational use
Repeatable workflows
Analytic summarisation feeding a supervisor-ready briefing.
Entity extraction and anomaly review inside a live investigation.
AI-assisted first pass followed by human correction and formal publication.
Jeder Tag ohne validierte KI ist akkumulierende Haftung
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