# Guidance for AI-Powered Vehicle Service Assistant using Amazon Bedrock

## Overview

This Guidance demonstrates how to implement an advanced multi-agent system to revolutionize automotive service and maintenance. It shows businesses how to integrate various data streams including connected mobility, diagnostics, scheduling, and parts management into a cohesive, AI-driven platform. The solution helps automotive companies provide proactive, context-aware assistance to drivers, enhancing vehicle performance and safety. It illustrates how to leverage natural language processing for improved user experience, enabling real-time issue detection and seamless service appointment scheduling. By showcasing the implementation, this Guidance helps organizations optimize their operations, reduce downtime, and significantly enhance customer satisfaction through predictive maintenance and timely interventions.

## Benefits

### Streamline vehicle service operations

Deploy an intelligent automotive service system that transforms manual diagnostics into an automated end-to-end experience. This solution helps reduce service center wait times and improves customer satisfaction by proactively identifying issues and scheduling appointments.


### Enhance driver experience

Provide drivers with immediate, voice-activated assistance for vehicle warnings and diagnostic issues while on the road. The solution delivers accurate problem identification and tailored guidance based on specific vehicle models, helping drivers make informed decisions about vehicle maintenance.


### Optimize parts management

Implement proactive parts procurement that automatically identifies necessary components based on diagnostic codes and checks real-time inventory. This approach minimizes service delays by ensuring parts availability when vehicles arrive for service, improving dealership efficiency and customer satisfaction.


## How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

[Download the architecture diagram](https://d1.awsstatic.com/onedam/marketing-channels/website/aws/en_US/solutions/approved/documents/architecture-diagrams/ai-powered-vehicle-service-assistant-using-amazon-bedrock.pdf)

![Architecture diagram](/images/solutions/ai-powered-vehicle-service-assistant-using-amazon-bedrock/images/ai-powered-vehicle-service-assistant-using-amazon-bedrock-1.png)

1. **Step 1**: While driving a dashboard warning appears, the driver activates smart assistant using a voice command. The system captures both the diagnostic data and converts the driver's voice request to text. This information flows through Amazon API Gateway, which securely authenticates and routes both inputs to AWS Lambda and invokes Amazon Bedrock orchestrator agent for immediate processing and response generation.
1. **Step 2**: The Amazon Bedrock orchestrator (supervisor) agent receives input, uses instructions to understand the input, and manages and delegates tasks required to the group of Amazon Bedrock collaborator agents.
1. **Step 3**: OEM manuals are ingested in Amazon S3. Embeddings are stored in vector databases, provided by Amazon OpenSearch. The Vehicle Symptom Agent uses Retrieval Augmented Generation (RAG) to analyze the issue, recommend diagnostic steps, and assess severity (Low/Medium/High). This enables rapid, accurate problem identification and tailored guidance for the driver based on the specific vehicle model and reported symptoms.
1. **Step 4**: The group of Amazon Bedrock collaborator agents, including dealership agent, dealership availability agent, appointment agent, parts agent, receive tasks from the supervisor agent in parallel and as supervisor agent's predictions require.
1. **Step 4a**: The collaborator agents use AWS Lambda actions to enable integrations with data sources and perform reads and writes to data sources in real-time. These data sources include Amazon DynamoDB that provides dealership information, availability, book appointments and parts availability based on diagnostic information
1. **Step 4b**: The group of collaborator agents invoke Amazon Nova (Pro, Lite) models as needed.
1. **Step 5**: The parts agent functions as a mini-orchestrator, coordinating multiple sub-functions to identify necessary automotive parts based on diagnostic codes. It checks real-time inventory at dealerships and automatically initiates orders for out-of-stock items to minimize service delays. This proactive approach ensures parts availability when the vehicle arrives for service.
[Read usage guidelines](/solutions/guidance-disclaimers/)