Bruviti | Aftermarket AI

Unified Data

AIP ingests service, asset, parts, and policy data, mapping both structured and unstructured inputs into a query-ready ontology. This ontology connects assemblies, parts and alternates, error codes, policies, and diagnostic procedures into a usable knowledge graph.

With this foundation, every service input — whether a log file, claim form, or sensor signal — becomes addressable by reasoning models and agentic workflows. AIP makes diverse service data speak the same language, ready for AI agents and models to use.

Logic processing representing domain intelligence

Domain Logic

Rules and small language models (SLMs) trained on aftermarket operational data evaluate millions of inputs to select the best path to resolution. SLMs far outperform general-purpose models for service-specific tasks.

SLMs include:

Installed base models predict maintenance or contract obligations
Policy models interpret entitlements and service rules
Warranty forecasting models estimate claim probability and coverage
Parts prediction models identify which component needs replacement
Triage models map symptoms and codes to likely root causes
Computer vision models execute visually when APIs are not available, recognizing UI elements and navigating screens like a human

Action execution representing autonomous agents

Agentic Action

Agentic workflows execute through a hierarchy of agents, workflows, tasks, and tools, matching natural business processes and scaling independently while remaining modular and reusable.

Agents orchestrate full business processes for example warranty resolution agent
Workflows coordinate multi-step procedures that combine tasks such as dispatch process or claim approval
Tasks perform atomic business operations such as validating entitlement or extracting an order number
Tools provide reusable functions and integrations like database connectors, API calls, or vision models

Event-driven architecture routes new service events like equipment errors to the right AI agents. Agents run in parallel, adapt as constraints change, and remain loosely coupled so new workflows can be added without reengineering.

Assurance

Every component in AIP from generated code to deployed workflows is continuously validated through a native evaluation framework.

Automated tests run after each generation cycle to validate functionality and performance
Human-in-the-loop reviews add domain-specific knowledge and catch edge cases
Iterative refinement raises quality with each cycle until production standards are met
Quality gates ensure accuracy, performance, and compliance before release

All agents, workflows and models in AIP are monitored, tested, and improved as they operate.

Aftermarket Ontology

AIP is built on a purpose-built aftermarket ontology that turns complex service data into a unified knowledge graph. The ontology encodes domain knowledge into structured, actionable concepts that AI agents can use to execute workflows.

Ontology is layered to capture all aspects of the aftermarket domain:

Standard modules for assets, parts, assemblies, documents, personnel, policies, workflows, and diagnostics.

Maps real-world equipment, service cases, and transactions into these reference concepts.

Applies predictive analytics, business rules, and reasoning models across the graph.

Exposes the entire ontology through GraphQL or REST, making data accessible to agents and AI models.

This design makes heterogeneous inputs such as error codes, logs, entitlement policies, and inventory updates addressable in a consistent structure. It allows agents to connect cause such as symptoms and codes to resolution such as replace a part or approve a claim rather than just surfacing related documents.

Platform Architecture

AIP is built on a modular architecture for scale, reuse, and enterprise integration. It combines an event-driven foundation with pre-built components and tools that make agentic automation practical.

Enterprise Ready

AIP runs in your environment delivering full control of data, logic, and IP. It provides built-in security, audit, and compliance. The architecture supports high availability, fault tolerance, elastic scaling, and performance at enterprise scale. Rapid development is built in through code generation and reusable components. AIP integrates with modern and legacy systems and works alongside existing enterprise AI initiatives.