Copilot accept rate: Sainapse-recommended resolutions applied by engineers without edits
FordUse case
An AI layer for enterprise IT operations
Sainapse is an AI layer that runs on the ITSM estate you already have: it correlates alerts into single incidents, classifies and routes every ticket, and drafts the next-best resolution inside ServiceNow for an engineer to apply. Ford has run it in production for four years, across 300+ specialists and 250K+ tickets a year.
The problem
Alert noise buries the operation before anyone resolves anything.
An IT estate handling 250K+ tickets a year rarely has a resolution problem. It has a volume problem that becomes one: the same incident arrives as alerts from several monitoring tools and spawns duplicate tickets before anyone sees it.
Engineers burn hours separating signal from noise before triage starts. Quality assurance is a spot check on a sample nobody claims is representative. The runbook library drifts, because the senior engineers who could keep it current have the least slack. And a copilot that lives inside one tool answers from the silo that raised the alert, with no view of what the other systems already said.
Triage and routing accuracy: every auto-created ticket classified and routed, no engineer in the loop
FordEngineer capacity reclaimed and reinvested, not a headcount change
FordProof
Ford has run Sainapse in production for four years.
Four years live, natively inside ServiceNow
Ford runs Sainapse across 300+ specialists and 250K+ tickets a year. Hand-offs per resolved ticket went from 4 → 1.2: a 70% collapse in resolution effort (alert to close, automated steps excluded). User satisfaction moved +35% concurrent with the Sainapse period: a correlation, not a causal claim.
In their words
Arunachalam, Director, IT · Ford
“Sainapse is the layer our 300-strong operations team actually trusts. 95% of the resolutions it recommends are applied without edits. Incidents that used to bounce through four hand-offs now close in just over one. Our engineers spend their time on the failures that actually need them.”
How it works
How one alert moves through the layer
Watch: alerts correlate before they become tickets
Every alert from every monitoring tool is ingested and correlated. Duplicates are identified and suppressed before a ticket exists, so one incident raises one ticket instead of twenty.
Decide: every actionable alert becomes a routed ticket
Actionable alerts convert into ServiceNow tickets, classified and routed at accuracy approaching 100% with no engineer in the loop. That is where ~15% of engineer capacity comes back, reinvested into SLA attainment and backlog.
Draft: the next-best resolution, rendered in ServiceNow
Sainapse reads across the ITSM record and the CRM and ERP systems the incident touches, then drafts the resolution inside the ServiceNow UI. Nothing executes autonomously: an engineer dispatches every one.
Learn: every resolution feeds the knowledge layer
Each resolved incident updates knowledge: new SOPs generated from resolution patterns, overlapping runbooks merged, deprecated ones flagged. The knowledge base evolves alongside the estate instead of drifting behind.
Three ways an IT organization absorbs alert volume
The comparison that matters is not vendor against vendor. It is how the work gets done: by hand, by a copilot that sees one system, or by a layer across the estate.
| Manual queues | Single-system copilot | Sainapse | |
|---|---|---|---|
| Duplicate alerts | By hand, ticket by ticket | Deduplicated inside one tool | Correlated across every monitoring tool before a ticket exists |
| Triage and routing | Queue owners route by convention | Routes what its own tool sees | Every auto-created ticket, no engineer in the loop |
| Context behind a draft | Whatever the engineer opens in another tab | One system's records | ITSM, CRM, and ERP records read together |
| Quality assurance | Spot checks on a sample | Model confidence, unaudited | Every recommendation accepted or edited by an engineer, logged |
| Knowledge upkeep | Runbooks drift behind the estate | A static article index | SOPs regenerated from resolution patterns as tickets close |
What it runs on
Runs insideServiceNow, as a native embed, so engineers stay in the console they already work in.
Powered byIntelligent Triage & Routing · Autonomous Resolution · Knowledge from Resolutions · End-to-End System Write
Reads fromThe ITSM record, plus the CRM and ERP systems an incident touches, through Sainapse Connect
Native connectorsSix live, ServiceNow among them
AutonomyNone by default. Sainapse recommends; an engineer applies.
Live sinceFour years in production at Ford: 300+ specialists, 250K+ tickets/yr
Common questions
Three factors decide it. A production-grade dedup, triage, copilot and knowledge layer for a 300-person operation is a 12-18 month in-house project. The closed loop (every resolved incident refining the next decision across all four) is the expensive part, and it is integration work, not model work. Model selection, latency, guardrails and prompt-injection defense become your roadmap too.
Most fail at one of two points: they answer from a single system, or they need a clean knowledge base before they are useful. Sainapse correlates across every monitoring tool, reads the records an incident touches, and builds knowledge out of resolutions instead of requiring it up front.
No, nothing executes autonomously. Triage and routing run with no engineer in the loop, at accuracy approaching 100%; resolutions stay recommendations an engineer reviews and dispatches. At Ford, 95% are applied without edits. That accept rate is ServiceNow accept/reject telemetry, not a survey.
No. Sainapse learns inside your deployment, from your resolutions, for your estate: resolution data is not pooled into a model shared across customers. Model selection, retention terms, hallucination guardrails and prompt-injection defense are Sainapse's accountability, agreed per deployment rather than left on your roadmap.
Deployment follows the estate. Sainapse runs against the systems you already operate, and residency, hosting and retention are agreed per deployment rather than assumed, settled before anything connects. Engineers keep working in ServiceNow: no second console, and no parallel data set to maintain.
Keep reading
What this page runs on
How the AI layer works
- Autonomous resolution
What Sainapse drafts, what an engineer still ships, and the published ramp.
- Intelligent triage and routing
The classification and routing step behind the triage accuracy number above.
- Knowledge from resolutions
How SOPs get generated from resolution patterns instead of hand-maintained.
Reading and writing across systems
- End-to-end system write
What it takes to write into a system of record, not recommend.
- Sainapse Connect
The connector layer: six native integrations live today, ServiceNow among them.
- Cross-system customer context
The same reads-everywhere behaviour, aimed at customer-facing support.
The Ford deployment, and what sits around it
- Ford: cross-system deployment
The full customer story behind every number on this page.
- End-to-end ticket automation
The same layer on an external support desk, alert to close.
- Customer support workflow
The hub this use case belongs to, and the rest of support.
Explore
- All use cases
Every workflow Sainapse runs, by the problem it solves.