Product
Governed AI runbooks and self-healing for enterprise IT ops
Tickets and logs flow in → AI generates and refines runbooks → gated automation executes with your approval. Resolvify turns reactive ops into governed self-healing.
- AI runbook generation
- Ticket + log context understanding
- Self-healing with approvals
- Change-aware operations
Deep capabilities
The six pillars
Each capability ties back to MTTR, escalations, or risk—not just features.
AI runbook generation
What it is
AI converts tribal knowledge, documentation, and incident history into executable runbooks.
How it works
You provide runbook sources (wikis, past incidents, scripts). Resolvify ingests, structures, and generates step-by-step remediation flows with parameters and guardrails.
Why it matters
Reduces MTTR by turning implicit knowledge into repeatable automation. New incidents get resolved faster without manual runbook authoring.
Ticket + log context understanding
What it is
Resolvify reads and interprets tickets, alerts, and log context to route and remediate.
How it works
Connects to ITSM and observability tools. AI correlates ticket content, alert signals, and logs to identify root cause and select the right runbook.
Why it matters
Faster triage, fewer false escalations. L1 teams focus on edge cases instead of routine repeats.
Self-healing automation
What it is
Automated remediation with human oversight at every decision point.
How it works
Runbooks execute against your infrastructure (APIs, scripts, automation tools). You choose: suggest-only, approve-to-run, or auto for low-risk patterns.
Why it matters
L1 tickets auto-resolve before escalation. SRE hours go to strategy, not repeat fixes.
Governance & human-in-the-loop
What it is
Role-based access, approval workflows, and audit trails for every action.
How it works
Define who can approve what. Every runbook execution is logged—who triggered it, what changed, rollback available. Assist mode lets humans drive while AI suggests.
Why it matters
CIO peace of mind. Change risk stays controlled. Compliance and audit trails built in.
Change-aware operations
What it is
Respects change windows, freezes, and rollback patterns.
How it works
Integrates with change calendars. Blocks automation during freeze periods. Rollback patterns are defined and triggered when drift or failure is detected.
Why it matters
No surprises during change windows. Rollbacks happen in minutes, not hours.
Reporting & learning
What it is
Improvement over time—outcomes feed back into runbook refinement.
How it works
Success/failure data, MTTR trends, and escalation patterns flow into Resolvify. Runbooks are tuned based on what actually works in your environment.
Why it matters
Continuous improvement. Fewer failed remediations, better automation coverage over time.
How it works
Architecture & data flow
Simple flow: Sources → Understanding → Runbooks → Execution → Feedback. Here's where models run, how data is stored, and where approvals happen.
Sources
ITSM, monitoring, logs, runbooks
Understanding
AI correlates and routes
Runbooks
Generated & refined
Execution
Gated automation
Feedback
Outcomes → learning
Where models run
AI inference runs in your VPC or Resolvify-managed SaaS. Your data stays within your boundaries.
Data storage
Runbooks, execution logs, and audit trails stored per tenant. Encrypted at rest and in transit.
Where approvals happen
At execution time—before any change. Approvers get context, proposed actions, and one-click approve/reject.
ITSM writeback
Status updates, comments, and resolution notes are written back to ServiceNow, JSM, and other ITSM tools.
Governance & safety
Guardrails and controls
This is Resolvify's differentiation—and your CIO's peace of mind. Human-in-the-loop, audit-ready, change-aware.
Role-based access
- Define who can approve what
- Least-privilege by runbook and environment
- Integration with your IdP (SAML, OIDC)
Approval workflows
- Suggest-only: AI proposes, human executes
- Approve-to-run: human signs off before execution
- Auto for low-risk: predefined patterns run without approval
Audit trails
- Every action logged: who, what, when
- Rollback history and change attribution
- Export for compliance and forensics
Change windows & freezes
- Respect change calendars—no execution during freeze
- Rollback patterns triggered on drift or failure
- Configurable blackout windows per environment
Human-in-the-loop modes
Integrations & environment
Fits your stack
ITSM, monitoring, logs, automation tools. Deploy SaaS or in your VPC—your choice.
ITSM
- ServiceNow
- Jira Service Management
- PagerDuty
Monitoring & observability
- Datadog
- Prometheus
- Grafana
- Splunk
- Elastic
Automation tools
- Ansible
- Terraform
- Scripts (Bash, Python)
- Runbook tools
Deployment options
SaaS
Resolvify-managed. Quick start, we handle infra.
VPC
Runs in your cloud. Full control, your networking.
Networking: outbound API calls to your tools. No inbound from Resolvify unless you choose agent-based connectivity.
Outcome proof
Metrics and results
In typical environments, customers use Resolvify to reduce MTTR, automate L1 work, and reclaim SRE bandwidth.
40–60%
MTTR reduction
Typical for L1/L2 incidents
50–70%
Incidents auto-remediated
Before human touch
30–50%
Fewer escalations
SRE time reclaimed
15–25h
Hours saved per week
Per 10-person ops team
What teams say
"We cut L1 MTTR by half in the first quarter. The guardrails let us move from assist to auto without losing sleep."
"Change window protection was the killer feature. We finally have drift detection and rollback that doesn't require all-nighters."
Implementation
What you need to get started
Clear requirements, who's involved, and what we do vs what you do.
Tools & access
- ITSM and monitoring tool access
- Runbook sources (wikis, docs, scripts)
- Environment for staging/sandbox
Who's involved
- IT ops / NOC
- SRE or platform team
- Security (for approval workflows, RBAC)
Typical timeline
- Week 1: Discovery & setup
- Week 2–3: Connect & configure
- Week 4+: Assist mode
- Scale: Move to automation when ready
What we do vs what you do
Discovery, integration, guardrail design, training
Access to tools, runbook sources, approver assignments
Ongoing support, runbook refinement, model tuning
Feedback, success criteria, expansion decisions
FAQs
Common questions
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Is it safe to let AI run automation in production?
Resolvify is built for governance. You choose: suggest-only (AI proposes, human executes), approve-to-run (human signs off before every execution), or auto for low-risk patterns only. Every action is logged and auditable.
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Where does my data go?
Data stays within your boundaries. Resolvify runs in your VPC or Resolvify-managed SaaS with tenant isolation. We do not train on your data. Encryption at rest and in transit.
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What about change risk and freeze windows?
Resolvify integrates with change calendars and respects freeze periods. No automation runs during blackout windows. Rollback patterns are defined and can be triggered automatically on drift or failure.
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How does it fit with our existing tools?
We integrate with ServiceNow, JSM, PagerDuty, Datadog, Prometheus, and more. Resolvify sits alongside your ITSM and observability stack—we read from them and write back status and resolutions.
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What's the typical pilot timeline?
Week 1: discovery and setup. Week 2–3: connect tools and configure guardrails. Week 4+: assist mode. Scale to automation when you're ready. No big-bang rollout.
Ready to go deeper?
Talk to a solutions engineer or see a live runbook build. We'll show you exactly how Resolvify fits your stack.