The AI Operating Layer for Mission-Critical Service
Domain-specialized AI that plugs into your enterprise architecture.
One foundation for every service use case.
The Production AI Factory.
Data, reasoning, action and control, built once and reused everywhere.
Unified data.
The AI Operating Layer ingests service, asset, parts and policy data, mapping structured and unstructured inputs into a query-ready service ontology. The ontology connects assemblies, parts and alternates, error codes, policies and diagnostic procedures in one usable knowledge graph.
With this foundation, the context behind every service input, whether a log file, claim form or sensor signal, can be used for precise reasoning by models and agents.
Domain reasoning.
Our portfolio of purpose-built service models, including open-weight options, runs alongside enterprise-approved models.
Trained specifically on operational service data, these verticalized service models reason more precisely for diagnostics, triage, prediction and classification, delivering more accurate answers than general-purpose frontier models.
Installed base
Predicts maintenance or contract obligations
Policy
Interprets entitlements and service rules
Warranty
Estimates claim probability and coverage
Parts prediction
Identifies which component needs replacement
Triage
Maps symptoms and codes to likely root causes
Computer vision
Navigates screens like a human where no API exists
Agentic action.
Agents execute through a hierarchy of workflows, tasks and tools. Each level scales independently and stays modular and reusable.
Event-driven architecture routes new service events, such as equipment errors, to the right agents. Agents run in parallel, adapt as constraints change and stay loosely coupled, so new workflows can be added without re-engineering.
Agent
- Warranty resolution
Workflow
- Claim approval
Task
- Validate entitlement
Tool
- Database connector
Control.
Every component, from generated code to deployed workflows, is continuously validated through a native evaluation framework.
All agents, workflows and models are monitored, tested and improved as they operate, to keep AI within the boundaries you set.
Test
- Validates functionality and performance
Review
- Adds domain knowledge and catches edge cases
Refine
- Raises quality with each cycle
Gate
- Checks accuracy, performance and compliance
Built around a service ontology.
Bruviti’s service ontology encodes equipment hierarchies, parts and procedures, policies, failure modes, tickets and service history as entities and relationships, creating a unified service knowledge graph that gives AI the context to reason precisely rather than guess.
Reference layer
Standard modules for the service domain
Instance layer
Maps real-world records into the reference concepts
Intelligence layer
Reasons across the whole graph
Unified query layer
Exposes the ontology to agents and AI models
A modular architecture for scale, reuse and governance.
An event-driven foundation with pre-built components for rapid deployment and reuse.
Customer experience
Customer chatbot
Voice agent
Federated search
Agent productivity
Case summaries
Next best action
Follow-up scheduling
Revenue + insight
Upsell agent
Reporting agents
Operational KPIs
Customer AI
Chat / Voice
Troubleshooting
Agent AI
Guidance
Case context
Decision AI
Policy / Next best action
Scheduling
Insight AI
Analytics
Forecasting
Knowledge graph
Service ontology
Asset 360
Domain models
NLU + SLMs
Policy models
Workflow engine
Agent orchestration
Evaluation + monitoring
Shared connectors
MCP endpoints
Pre-built for migration
Enterprise ready.
Bruviti’s AI Operating Layer runs in your enterprise environment and seamlessly connects with the systems you already use.
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Control
Runs in your environment, with full control of data, logic and IP.
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Security
Built-in security, audit and compliance.
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Scale
High availability, fault tolerance and elastic scaling.
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Speed
Studio editor and reusable components that integrate with modern and legacy systems.
