AI Services & Support Agents
Service & support agents that resolve
Most agents retrieve knowledge articles. Arango grounds your agents in your whole operational estate — runbooks, tickets, telemetry, and docs — so they diagnose the root cause and recommend the fix, with traceable sources.
Value Drivers
What Arango delivers for IT operations
01
Faster mean time to resolution
Grounded, source-cited next steps replace manual diagnosis across disconnected tools.
Target: 40–60% lower MTTR
02
More efficient use of resources
Engineers stop swivel-chairing across 5+ systems — the resolution path comes to them.
Higher first-contact resolution
03
Scale to thousands of incidents/day
Production-grade retrieval holds sub-10-second guidance under peak concurrency.
1,000s/day at <10s response
04
Fewer false leads, faster root cause
Grounds answers in real dependencies — symptom to root cause — so engineers chase the actual cause, not lookalike noise.
80%+ next-step accuracy
KPI ranges reflect industry AIOps benchmarks for intelligent incident management; actual results vary by environment.
Customer Outcomes
Proven in production
A global cybersecurity leader — a Fortune 500 zero-trust provider securing thousands of enterprises — replaced manual search across 5+ applications and a vector-only RAG attempt with a knowledge graph and multi-agent orchestration built on Arango. AI is now part of the daily support workflow.
40,000+
AI requests per day
<10s
response (down from 30s)
80%+
next-step accuracy
500+
runbooks & KBs operationalized
The takeaway: moving from “here are similar articles” to “here is the likely cause, what to check next, and the safest path to resolution” — reducing MTTR while scaling to enterprise volume.
How it Works
From symptoms to resolution
A support knowledge graph connects the case to its symptoms, configs, dependencies, known bugs, and fixes — and the safest path to resolve.
Why a graph, not vector-only?
Vector search says “these cases look similar.” The graph says “here is the likely cause, what to check next, and the safest path to resolution.”
The Matrix
From incident management challenges to measurable value
| Incident-management challenge | Arango Contextual Data Platform | Value · KPI / OKR |
|---|---|---|
| Knowledge siloed across 5+ tools — runbooks, tickets, KBs, telemetry | AutoGraph Studio turns runbooks, tickets, and KBs into one graph-native contextual data layer | Faster triage · lower MTTR |
| Vector-only RAG returns lookalike text, not fixes | GraphRAG reasons over relationships: symptom → config → dependency → root cause, with citations to source | 80%+ next-step accuracy · fewer guesswork loops |
| Engineers chase false leads and lookalike noise | Grounds AI in real dependencies; GraphML anomaly detection flags genuine deviations from normal | Fewer false leads · faster root cause |
| High MTTA — unclear ownership, slow triage | AutoGraph Studio surfaces relevant context (Instant Search) and runs multi-step diagnosis (Deep Search) | Lower MTTA · faster acknowledgement |
| Repeated escalations; tribal knowledge lost | AutoGraph Studio keeps prior cases + runbooks current with incremental graph updates | Fewer escalations · knowledge retained |
| Can’t scale support without adding headcount | AutoGraph Studio’s production-grade infrastructure scales retrieval to thousands of requests/day | Scale at <10s · flatter cost curve |
The Shift
Before and after Arango
Before
The Incident Desk Frankenstack
After
Arango Contextual Data Platform
Outcome
Response latency 30s → <10s
MTTR down
Escalations down
Knowledge reused, not rebuilt
Answers from all your knowledge — cited.
Point Arango at your wikis, SharePoint, and drives — and let your people ask. Every answer comes back grounded and traceable to source.