Turn hidden AI use into governed operations.
See where AI is already being used, who owns it, what evidence is missing, and what to do next.
Claims triage assistant
Operations
Assign owner
Support copilot
Service
Evidence gap
Payments risk model
Risk
Control check
Owners mapped
0
Evidence gaps
0
Actions open
0
Most teams lose AI in the handoffs.
Where is AI being used?
Who owns each use case?
What data and vendors are involved?
What evidence exists today?
The issue usually shows up between teams: business has the use case, procurement has the vendor file, security has a concern, and compliance is asked to approve without the full record.
Find the use case. Name the owner. See what is missing.
The first pass is practical: surface the use case, connect it to a responsible owner, and show the evidence gaps before they turn into back-and-forth.
Capture
Use cases, business context, owner candidates, vendor touchpoints, and data exposure.
Connect
Likely obligation triggers across AI Act, GDPR, DORA, NIS2, and internal policy.
Expose
Missing owners, unclear approvals, weak documentation, and controls that need proof.
Prioritize
Next actions that make the use case reviewable by the right people.
Hands-on where judgment matters. Productized where the work repeats.
RegArq starts with structured support around real AI use cases. As patterns repeat, the workflow becomes reusable intake, routing, evidence, and review infrastructure.
Service-enabled at launch
Structured intake, AI-assisted mapping, and expert review help teams move from scattered AI use to clear next actions.
Product-led over time
Repeated ownership paths, evidence gaps, and review patterns become reusable workflows and product structure.
Measured from day one
Each engagement tracks time to first Action Map, review effort, delivery cost, and which steps repeat.
The operations layer under the Action Map.
Under the first artifact is a repeatable path: intake, trigger mapping, evidence routing, expert review, and a maintained action ledger.
Operations path
01 Signal intake
Capture live use
Use cases, vendor context, team notes, data touchpoints, and existing evidence enter one record.
02 Classification
Draft the trigger map
The system structures likely AI Act, GDPR, DORA, NIS2, policy, and ownership questions for review.
03 Evidence routing
Turn gaps into work
Missing approvals, oversight notes, vendor evidence, and monitoring records become assigned next actions.
04 Review gate
Keep judgment human
Expert review stays on regulatory interpretation, risk priority, edge cases, and customer-specific commitments.
Action Map output
owner
Customer operations
status
Review and assign
evidence
3 found / 3 missing
review
Expert gate required
Intake -> map -> route -> review -> maintain
Pilots show which steps stay human, which can be assisted, and which belong in product infrastructure.
From hidden use to operating work.
01
Discover
Capture AI use cases already showing up across teams, tools, vendors, and workflows.
02
Assign
Connect each use case to a responsible business owner and the teams needed to govern it.
03
Map
Identify data, vendor, policy, and regulatory touchpoints without overloading teams with legal detail.
04
Close gaps
Turn missing evidence, unclear approvals, and weak controls into assigned next actions.
05
Maintain
Keep ownership, status, and audit-ready evidence current as AI adoption keeps moving.
A good fit when AI use is already moving through the business.
What teams usually ask.
Who is this for?
Regulated teams that already see AI entering work through vendors, copilots, models, or internal tools.
What happens next?
RegArq reviews the note and follows up by email if there is a practical fit for a conversation, expert review, or focused engagement.
What stage is this?
RegArq is working with teams that need practical AI governance workflows around real use cases.
Is this legal advice?
No. RegArq focuses on operational governance: intake, ownership, evidence, controls, status, and next actions.
Why start here?
Ownership, evidence, and next actions need to be clear before a larger platform, advisory program, or internal build can work well.
Get one use case under control.
Request a conversation to map hidden use, clarify ownership, and identify the evidence your team needs to maintain governed operations.