Full automation with no oversight creates exposure you cannot defend in an audit. Assistive tools that still need a human for every step create no leverage at all. Useful automation sits between them, and the line has to be drawn on purpose.
01
Volume, handled
Continuous monitoring and triage across endpoints, networks, and security posture. Routine events resolve without anyone initiating them.
02
Judgment, escalated
Anything touching regulated data, financial authority, or access rights routes to a named person before it executes. You set the threshold.
03
Everything, recorded
Every automated action and every human override written to an audit trail — which is what turns automation from a risk into evidence.
Where it pays
Workflows that return time immediately.
Employee lifecycle
Days to hours
A record created in your HR system triggers device provisioning, access grants, and licence assignment — no coordination meeting required.
Compliance evidence
Continuous, not annual
Control evidence collected as work happens rather than assembled under deadline before an audit.
Integrations
Connects to what you already run.
Productivity
Microsoft 365
Entra ID, Intune, Purview
Identity
Okta, Duo, JumpCloud
Single sign-on and conditional access
Service desk
ConnectWise
Ticket routing and reporting
Orchestration
N8N + Claude
Workflow automation with a reasoning layer
Governance
Every AI action is reviewable, not just explainable.
"Responsible AI" isn't a disclaimer here — it's the mechanism. These six controls apply to every deployment, on every client.
Human-in-the-loop approvals
Confidence thresholds you set determine when a person signs off before an action executes — not after.
Full audit trail
Every AI decision and every human override is logged, timestamped, and tied to a compliance record.
Data minimization & encryption
Models see only the data they need for the task at hand. Everything at rest and in transit is encrypted.
Role-based access control
AI agents operate under the same least-privilege model as your staff — nothing more, nothing implicit.
Evaluation & red-teaming
Models are tested against adversarial prompts and bias checks before they touch a production workflow.
Disclosure & versioning
You always know which model version made a decision, and exactly when it changed.
Use cases
Built for the constraints regulated organizations actually have.
The same automation model, applied where the stakes and the paperwork are highest.
Healthcare
HIPAA-scoped access requests route to a human before they're granted; every approval is written to the compliance record.
Access review: days → same day
Financial services
Compliance review workflows that used to take days surface AI-flagged items for a human reviewer the same day they're raised.
Manual review cycles: 4 days → under 6 hours
Nonprofits
New-hire onboarding — device provisioning, SaaS access, MFA — completes automatically instead of over several days.
Onboarding: 3 days → under 4 hours
Education
FERPA-aware automation handles routine IT tickets so staff spend time with students, not resetting passwords.
Tier 1 tickets requiring a human: down significantly
Security & compliance
Secure and compliant by default, not by request.
The platform-level controls every pillar inherits — the same baseline whether the workflow is a password reset or a compliance-scoped access change.
Will AI make decisions about our compliance-sensitive data on its own?
No. Anything scoped as sensitive or high-risk routes to a human reviewer by default. You control the confidence thresholds that decide when that review is required.
What happens if the AI gets something wrong?
Every action — AI or human — is logged with enough detail to trace exactly what happened and who reviewed it. Where an action is reversible, the audit trail shows the reversal too.
Do you use our data to train models?
No. Your data stays scoped to your environment. We do not train models on client data across accounts.
How does this fit with our existing HIPAA or SOC 2 program?
It extends your existing audit evidence rather than replacing your compliance process — the same frameworks your team already reports against.
How long does it take to stand up Managed AI for our environment?
Typical rollout is phased over several weeks: assessment, a scoped pilot on one workflow, then expansion once the confidence thresholds are tuned to your environment.
Get started
Talk to an engineer, not a sales rep.
Bring us a process that is costing time.
We will map what automation can take off your team's plate, and what it should not touch.
