Vxceed delivers SaaS solutions across industries such as consumer packaged goods (CPG), transportation, and logistics. Its modular environments include Lighthouse for CPG demand and supply chains, GroundCentric247 for airline and airport operations, and LimoConnect247 and FleetConnect247 for passenger transport. These solutions support a wide range of customers, including government agencies in Australia and New Zealand.
In 2024, Vxceed launched a strategy to integrate generative AI into its solutions, aiming to enhance customer experiences and boost operational efficiency. As part of this initiative, Vxceed developed LimoConnectQ using Amazon Bedrock and AWS Lambda. This solution enables efficient document searching, simplifies trip booking, and enhances operational decisions while maintaining data security and protection.
The challenge: Balancing innovation with security
Vxceed’s customers include government agencies responsible for transporting high-profile individuals, such as judiciary members and senior officials. These agencies require highly secure systems that adhere to standards like Information Security Registered Assessors Program (IRAP), used by the Australian government to assess security posture.
Government agencies and large corporations that handle secure ground transportation face a unique challenge: providing seamless, efficient, and secure operations while adhering to strict regulatory requirements. Vxceed Technologies, a software-as-a-service (SaaS) provider specializing in ground transportation and resource planning, recognized an opportunity to enhance its LimoConnect solution with generative AI. Vxceed initially explored various AI solutions but faced a critical hurdle: verifying that customer data remained within their dedicated private environments. Existing AI offerings often processed data externally, posing security risks that their clients could not accept.
Vxceed needed AI capabilities that could function within a highly controlled environment, helping to ensure complete data privacy while enhancing operational efficiency.
This challenge led Vxceed to use Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies through a single API.
LimoConnect Q solution overview and implementation highlights
To address the challenges of secure, efficient, and intelligent ground transportation management, Vxceed developed LimoConnect Q, an AI-powered solution. LimoConnect Q’s architecture uses Amazon Bedrock, Amazon API Gateway, Amazon DynamoDB, and AWS Lambda to create a secure, scalable AI-powered transportation management system. The solution implements a multi-agent architecture, shown in the following figure where each component operates within the customer’s private AWS environment, maintaining data security, scalability, and intuitive user interactions.
Figure 1 – Vxceed’s LimoConnect Q architecture
Let’s dive further into each component in this architecture:
Conversational trip booking with intelligent orchestration using Amazon Bedrock Agents
Beyond document queries, LimoConnect Q revolutionizes trip booking by replacing traditional forms and emails with a conversational AI-driven process. Users can state their trip requirements in natural language. Key features include:
Natural language: Processes natural language booking requests based on travel context and preferences, for example:
Schedule airport pickup for dignitaries at 9 AM tomorrow to the conference center.
Book airport to my office transfer next Monday at 10 AM.
Automated data retrieval and processing: LimoConnect Q integrates with multiple data sources to:
Validate pickup and drop-off locations using geolocation services
Automates address geocoding and external API lookups, verifying accurate bookings.
Verify vehicle and driver eligibility through Amazon Bedrock Agents
Retrieve relevant trip details from past bookings and preferences
Seamless booking execution: After the request is processed, LimoConnect Q automatically:
Confirms the trip
Provides personalized recommendations based on booking history
Sends real-time booking updates and notifies relevant personnel (for example, drivers and dispatch teams)
This conversational approach minimizes manual processing, reduces booking errors, and enhances user convenience—especially for busy professionals who need a fast, friction less way to arrange transportation.
Secure RAG for policy and document querying using Amazon Bedrock Knowledge Bases
One of the most critical functionalities of LimoConnect Q is the ability to query policy documents, procedural manuals, and operational guidelines in natural language. Traditionally, accessing such information required manual searches or expert assistance, creating inefficiencies—especially when expert staff aren’t available.
Vxceed addressed these challenges by implementing a Retrieval Augmented Generation (RAG) framework. This system generates responses that align with policies, incorporate relevant facts, and consider context. The solution delivers the ability to:
Query documents in natural language: Instead of searching manually, users can ask questions like What is the protocol for VIP pickup at the airport?
Restrict AI-generated responses based on RAG: Use RAG to make sure that answers are pulled only from approved, up-to-date documents, maintaining security and compliance.
Keep sensitive data within the customer’s environment: LimoConnect Q maintains data privacy and compliance by keeping queries within the customer’s private AWS environment, providing end-to-end security.
This capability significantly improves operational efficiency, allowing users to get instant, reliable answers instead of relying on manual lookups or expert availability.
Multi-agent AI architecture for secure orchestration
Vxceed built a multi-agent AI system on Lambda to manage LimoConnect Q’s transportation workflows. The architecture comprises agents that handle dispatch, routing, and scheduling tasks while maintaining security and scalability.
Intent recognition agent: Determines whether a user request pertains to document retrieval, trip booking, or another functions.
Document retrieval agent: Handles policy queries using RAG-based retrieval.
Trip booking agent: Processes user inputs, extracting key information such as pickup and drop-off locations, time, vehicle type, passenger count, and special requests. It verifies that booking information is provided, including name, contact details, and trip preferences. The agent validates addresses using geolocation APIs for accuracy before proceeding. The agent then checks vehicle and driver availability by querying the fleet management database, retrieving real-time data on approved resources. It also interacts with a user preference database, using vector-based search to suggest personalized options.
Flight information validation agent: Verifies flight schedules.
Trip duplication agent: Checks for previously booked trips with similar details to help avoid duplicate bookings.
Return trip agent: Analyzes past trips and preferences to recommend suitable return options, considering real-time vehicle availability and driver schedules.
Data validation agent: Verifies security policy compliance.
External API agent: integrates with third-party services such as geolocation services, scheduling interfaces, and transportation databases, providing real-time data updates for optimized trip coordination.
Booking retrieval agent: Helps users retrieve existing bookings or cancel them, querying the backend database for current and past trips.
After validation, LimoConnect Q uses Lambda functions and Amazon Bedrock integrated APIs to process bookings, update databases, and manage notifications to drivers and dispatch teams. The modular architecture enables Vxceed to seamlessly add new features like driver certification tracking and compliance automation.
Built with security at its core, LimoConnect Q uses Lambda for efficient handling of query spikes while implementing robust memory isolation mechanisms. Each user session maintains temporary memory for contextual conversations without permanent storage, and strict access controls ensure session-specific data isolation, preventing cross-contamination of sensitive information. This architecture adhere to the stringent security requirements of government and enterprise customers while maintaining operational efficiency.
Using LimoConnect Q, customers have saved an average of 15 minutes per query, increased first-call resolution rates by 80 percent, and cut onboarding and training time by 50 percent.
Guardrails
LimoConnect Q uses Amazon Bedrock Guardrails to maintain professional, focused interactions. The system uses denied topics and word filters to help prevent unrelated discussions and unprofessional language, making sure that conversations remain centered on transportation needs. These guardrails constrain the system’s responses to travel-specific intents, maintaining consistent professionalism across user interactions. By implementing these controls, Vxceed makes sure that this AI solution delivers reliable, business-appropriate responses that align with their customers’ high standards for secure transportation services.
AI-powered tools for ground transportation optimization
LimoConnect Q also incorporates custom AI tools to enhance accuracy and automation across various transportation tasks:
Address geocoding and validation: AI-powered location services verify pickup and drop-off addresses, reducing errors and maintaining accurate scheduling.
Automated trip matching: The system analyzes historical booking data and user preferences to recommend the most suitable vehicle options.
Role-based access control: AI-driven security protocols enforce policies on vehicle assignments based on user roles and clearance levels.
These enhancements streamline operations, reduce manual intervention, and provide a frictionless user experience for secure transportation providers, government agencies and large enterprises.
Why Vxceed chose Amazon Bedrock
Vxceed selected Amazon Bedrock over other AI solutions because of four key advantages:
Enterprise-grade security and privacy: Amazon Bedrock provides private, encrypted AI environments that keep data within the customer’s virtual private cloud (VPC), maintaining compliance with strict security requirements.
Seamless AWS integration: LimoConnect Q runs on Vxceed’s existing AWS infrastructure, minimizing migration effort and allowing end-to-end control over data and operations.
Access to multiple AI models: Amazon Bedrock supports various FMs, allowing Vxceed to experiment and optimize performance across different use cases. Vxceed uses Anthropic’s Claude 3.5 Sonnet for its ability to handle sophisticated conversational interactions and complex language processing tasks.
Robust AI development tools: Vxceed accelerated development by using Amazon Bedrock Knowledge Bases, prompt engineering libraries and agent frameworks for efficient AI orchestration.
Business impact and future outlook
The introduction of LimoConnect Q has already demonstrated significant operational improvements, enhancing both efficiency and user experience for Vxceed’s customers including secure transportation providers, government agencies and enterprise clients.
Faster information retrieval: AI-driven document querying reduces lookup times by 15 minutes per query, ensuring quick access to critical policies.
Streamlined trip booking: 97% of bookings now happen digitally, removing manual workflows and enabling faster confirmations.
Enhanced security and compliance: AI processing remains within a private AWS environment, adhering to strict government security standards such as IRAP.
Beyond government customers, the success of LimoConnect Q powered by Amazon Bedrock has drawn strong interest from private sector transportation providers, including large fleet operators managing up to 7,000 trips per month. The ability to automate booking workflows, improve compliance tracking, and provide secure AI-driven assistance has positioned Vxceed as a leader in AI-powered ground transportation solutions.
Summary
AWS partnered with Vxceed to support their AI strategy, resulting in the development of LimoConnect Q, an innovative ground transportation management solution. Using AWS services including Amazon Bedrock and Lambda, Vxceed successfully built a secure, AI-powered solution that streamlines trip booking and document processing. Looking ahead, Vxceed plans to further refine LimoConnect Q by:
Optimizing AI inference costs to improve scalability and cost-effectiveness.
Enhancing AI guardrails to help prevent hallucinations and improve response reliability.
Developing advanced automation features, such as driver certification tracking and compliance auditing.
With these collaboration, Vxceed is poised to revolutionize ground transportation management, delivering secure, efficient, and AI-powered solutions for government agencies, enterprises, and private transportation providers alike.
If you are interested in implementing a similar AI-powered solution, start by understanding how to implement asynchronous AI agents using Amazon Bedrock. See Creating asynchronous AI agents with Amazon Bedrock to learn about the implementation patterns for multi-agent systems and develop secure, AI-powered solutions for your organization.
About the Authors
Deepika Kumar is a Solution Architect at AWS. She has over 13 years of experience in the technology industry and has helped enterprises and SaaS organizations build and securely deploy their workloads on the cloud securely. She is passionate about using Generative AI in a responsible manner whether that is driving product innovation, boost productivity or enhancing customer experiences.
Cyril Ovely, CTO and co-founder of Vxceed Software Solutions, leads the company’s SaaS-based logistics solutions for CPG brands. With 33 years of experience, including 22 years at Vxceed, he previously worked in analytical and process control instrumentation. An engineer by training, Cyril architects Vxceed’s SaaS offerings and drives innovation from his base in Auckland, New Zealand.
Santosh Shenoy is a software architect at Vxceed Software Solutions. He has a strong focus on system design and cloud-native development. He specializes in building scalable enterprise applications using modern technologies, microservices, and AWS services, including Amazon Bedrock for AI-driven solutions.
