AI
Run governed AI inside the MSP workflow instead of bolting generic chat onto separate tools.
The current AI surface already includes specialist agents, provider-chain management, task routing, autonomy policies, inline actions, approval cards, dispatch, queued jobs, verification, frontdoor intake, resolution-ledger tracking, insights, and ROI reporting across PSA, RMM, IAM, backup, cloud, and billing workflows. Permission scopes already govern whether an agent can read data, update ticket fields, assign or merge work, run queue diagnostics, or draft artifacts.
- Suggestions, approvals, dispatch, and verification stay attached to the same job.
- Operators can see what AI did, what it asked for approval on, and what changed afterwards.
Before / After
AI stops being a side experiment and starts acting inside the operating model.
Before: teams paste tickets and alerts into disconnected AI tools, then manually translate the answer back into the queue. After: the AI layer lives inside the same governed product, so routing, approvals, execution, and verification stay attached to the work itself.
- Use specialist agents instead of one generic prompt surface
- Keep approvals, dispatch, and verification tied to the same queue and device context
- Measure AI impact with resolution-ledger tracking and ROI reporting instead of anecdotes
What the AI product already covers
Agents Specialist agents for tickets, ops, IAM, backup, and cloud work The AI layer is already structured around distinct MSP workflows rather than one generic chatbot.
Each specialist agent can already be enabled, disabled, and permission-scoped so operators can decide whether AI may read data, update ticket fields, assign or merge work, run queue diagnostics, or draft artifacts.
Providers Provider-chain management with BYO fallback and task-by-task routing The code already includes a real provider manager and routing layer, not a single hard-coded model choice.
- Multiple providers in the AI chain
- Bring-your-own provider fallback handling
- Task routing that assigns a provider and model per named AI task
Guardrails Autonomy policy engine and approval cards for controlled execution AI work already moves through explicit guardrails instead of running as an ungoverned side feature.
- Suggest-only, approval-required, and autonomous modes
- Approval cards with rationale and approve or reject handling
- Permission-aware AI execution tied to operator policy and action type
Dispatch Inline actions, manual dispatch, queued jobs, and verification workflows The AI layer already goes beyond draft text into governed action dispatch and verification.
- Inline AI actions beside operational workflows
- Dispatch console for PSA, RMM, IAM, backup, cloud, and billing actions such as script execution, device isolation, threat whitelisting, reboot-and-patch, firewall changes, backup restore, identity changes, license changes, and secret reveal requests
- Queued job visibility plus explicit verification runs against ledger items
Frontdoor AI frontdoor sessions for intake, follow-up, and ticket-created flows AI already has a customer-facing intake path, not just internal technician actions.
The frontdoor workflow already creates sessions, queues work, and follows the state from intake to follow-up and ticket-created handling instead of leaving AI detached from the service flow.
Measure Resolution ledger, insights, and ROI reporting for AI outcomes The product already measures AI work rather than treating AI as an untracked overlay.
- Resolution ledger rows tied to autonomy mode and action history
- Enterprise AI insights for current operational context
- ROI summaries to quantify queue and operator impact
See how governed AI fits your service and operations workflow.
We can walk through specialist agents, provider routing, approvals, dispatch, verification, frontdoor intake, and ROI tracking against the way your team wants AI to actually operate.