Solution / Business use case
Fewer repetitive tickets. Faster, calmer incidents.
Service desk automation, incident assistance and engineering knowledge, connected to your tooling.
IT & Engineering
/ 01Challenge
What gets in the way today
IT teams handle large volumes of repetitive requests while also keeping complex systems running. During incidents, engineers lose time piecing together logs, alerts and history.
Engineering knowledge lives in code, tickets and chat, and leaves when people move on.
/ 02AI workflow
How the work flows with AI in it
Steps marked human are decision points owned by your people.
- 01
Triage
Tickets and alerts are classified, deduplicated and enriched with context.
- 02
Resolve routine
Standard requests such as access or resets are fulfilled through approved automations.
- 03
Assist incidents
Timelines, related changes and relevant runbooks are assembled for responders.
- 04human
Confirm
Engineers approve remediation steps; nothing changes production without them.
- 05
Capture knowledge
Post-incident summaries and resolutions are indexed for next time.
/ 03Example architecture
Signals in, governed outcomes out
A typical reference design. Your version depends on your systems, data and risk appetite.
- ITSM tickets
- Monitoring alerts and logs
- Runbooks and wikis
- Code repositories
- Classification and deduplication
- Timeline assembly
- Runbook retrieval
- Approved automations
- Change approval for production actions
- Least-privilege service accounts
- Secrets never exposed to models
- Full action logs
- Resolved tickets
- Incident briefs
- Draft post-incident reviews
- Searchable engineering knowledge
/ 04Benefits
What changes, and how you would measure it
Lower ticket load
Routine requests are fulfilled automatically with an audit trail.
Faster incident response
Responders start with an assembled picture instead of a blank page.
Retained knowledge
Resolutions and decisions remain searchable after people move on.
Measures we would agree with you
- Mean time to resolve
- Share of tickets auto-fulfilled
- Incident time to diagnosis
- Repeat incident rate
We do not quote generic improvement figures. Targets are set from your measured baseline.
Limitations to plan for
- Automated remediation in production should start read-only and expand only with proven reliability.
- Code assistants must be governed for licensing and secret leakage.
/ 05Product and service match
What we would use
Product concepts
- Product conceptAI Agent WorkspaceA governed workspace where AI agents take on multi-step work with people approving what matters.
- Product conceptEnterprise Knowledge CopilotGrounded answers from your own documents and systems, with sources, permissions and clear uncertainty.
- Product conceptConversational & Voice AIChat and voice assistants that resolve routine requests and hand over cleanly to people.
Services
- Service / buildAgentic AI & Multi-Agent SystemsAgents that use tools to complete multi-step work, engineered with limits, approvals and evaluation.
- Service / operateEvaluation, Guardrails & ObservabilityKnow how your AI systems behave: test suites, runtime guardrails, tracing and quality monitoring.
- Service / integrateAPI & Legacy System ModernisationExpose legacy capabilities through clean, secure APIs so AI and modern apps can use them safely.
/ 07FAQ
Questions buyers ask
Can an AI agent fix production incidents on its own?
We start with read-only assistance and human-approved actions. Autonomous remediation is only appropriate for narrow, well-tested cases with automatic rollback.
Does this work with our ITSM platform?
Integration with platforms such as ServiceNow, Jira Service Management and Freshservice is done through their APIs.
Explore AI for it & engineering
Bring a sample of the work: tickets, documents, requests. We will show you what is realistic and how we would measure it.