Don't just monitor AI. Manage it as part of Legal's operation.
Orchestration puts every agent inside the department's real workflows. Governance gives IT, Legal Ops and management the answers they need to say yes. Both are already part of mot-r, for the agents your team builds and the ones we build with you.
Three questions stand between a DIY agent and production.
"Is it safe?"
Where data goes, who can see it, which models are in use.
With mot-r: access that follows existing permissions, approved models only, and a full record of AI activity.
"Will it keep working?"
Tools that break, depend on one person, or never get past a pilot.
With mot-r: DIY tools run inside governed, repeatable workflows the team can maintain and improve.
"Is it worth it, and who is accountable?"
Risk, accountability and proof of value.
With mot-r: human approval where it matters, an audit trail behind every decision, and AI usage and cost visible as standard.
Agents inside the work, not beside it.
An agent that produces an answer in a chat window still leaves the routing, the decisions and the follow-up to someone else. In mot-r, AI steps sit in the same workflow as the people around them.
Request comes in
Routed to the right people
AI prepares the work
A lawyer reviews and approves
Hand-off and close
Recorded at every step: who did what, what the AI did, and what was approved.
Controls, by design.
Switch AI on or off
AI is a module. Workflows keep running without it.
Bring your own key
Use your own AI accounts, isolating and controlling your risk with each provider.
Any model, by task
Run several providers at once and change them without rebuilding.
Scoped access
Each AI task does only what it is defined to do, with internet and DMS access set per task. Documents stay in your DMS, inside your existing security model.
Approvals AI can't bypass
Where a step needs a person's sign-off, the AI waits for it.
Evidence of AI activity
Every AI action sits in the workflow history, beside the human decisions around it.
Usage and cost, visible
Adoption, consumption and performance by provider, model, task and user, as standard.
Owned by the department
Workflows, prompts and responsibilities change by point-and-click, so the capability survives people changes.
See where AI is working, not just what it consumed.
A standalone AI dashboard reports tokens and latency. Because mot-r records AI activity as part of the work, it ties every AI call to the task, workflow and job it served, and to the people around it.
| What leaders can see | What it tells them |
|---|---|
| Adoption | Which people and teams are using AI |
| Consumption | Input, output and cached tokens, and what they cost |
| Providers and models | Usage across providers and models, side by side |
| Performance | Success rates, response times and cache efficiency by model |
| Activity in the work | Each AI call tied to the task, workflow and job it served |
| Trends | How AI use changes over time, by model and by task |
| Accountability | Who, what, when, which model and the outcome, for every AI action |
That's not AI in legal work. That's AI governance for legal work.
Building AI policy, risk assessments or approval criteria? Start with the AI Governance Hub.
Prove it in 60 days.
One real workflow, measures agreed in advance, and the evidence your approvers need.