Your Production AI Blueprint
Bring one service AI use case. We’ll do the technical design work and walk your team through the Blueprint in a 1-hour session.
Service AI knowledge graphEntity model
Has problemHas causeResolved by
FeatureDocumentSpecification
Model
Accessory
SymptomError code
Problem
SubsystemPart
Diagnostic topicFailure mode
Cause
Source trace
ProcedureTool
Solution
DiagnosticsSafety
Agents define, build and traverse the graph
How it works
+Setup
Define the use case
Tell us the service problem and outcome you want to work on.
We build the Blueprint
Our deployment engineers do the technical design work around your use case.
→
+Deliver
Your 1-hour Blueprint session
Our forward-deployed engineers meet your team, in person or virtually, present the Blueprint and walk you through the design.
What your Blueprint answers
+Operational design
How should this use case work in the real service workflow?
TriggerDecision pointsHuman approval
Your sourcesRaw exports · CSV / XLSX
Network TrackerSalesforce CasesDispatchDMS / FAWMS / ShipmentService ReportingAPLMBusiness Rules
Bruviti AI layerAzure-isolated VNet · No public endpoints
Ingest & Normalize
Map sources to the ontology. Resolve entity links.
Ontology Store
Source-grounded + inferred entities.
Discovery Engine
Anomaly · repeat-behavior · root-cause mining.
Reasoning Layer
Entity-aware NL · trend · recommend.
Output surfaces
ASKBriefing memoDiscovery feedCustomer 360Use-case dashboardsExport
+Data and context
How should fragmented information be structured so the AI understands it?
Source systemsEntitiesRelationships
+AI and reasoning
How does it turn that information into an answer you can trust?
RetrievalRoutingConfidence
Build the eval set
- +Your SMEs upload Excel / CSV: questions + expected answers
- +Or chat with the platform and label good vs. bad answers
- +Lives in the platform as a versioned, traceable artefact
Run on every build
- +Every release scores against the eval set automatically
- +Per-question pass/fail · regression detection
- +Score trajectory chart your team can see at any time
Close the loop
- +Failures route back to ingestion / ontology / reasoning
- +Re-run after fix · regression catches future drift
- +Baseline tightens as your golden set grows
Why it matters
An eval set is how you hold the platform accountable, and how we keep it accurate as the data and the questions change.
One use case. AI engineer-designed. Yours to keep.
Bring us a service AI problem you’re already working on. We’ll build the Blueprint and walk your team through it.