Managing a fleet of 75,000 vehicles is a massive operational feat, but for one leading logistics technology company, the real challenge wasn’t on the road—it was hidden inside their servers.

Context

A logistics technology company’s delivery and fleet-orchestration platform grew rapidly, but its operational data became fractured across three distinct environments: Amazon RDS, MongoDB, and Amazon S3. Managing a connected fleet of 75,000 vehicles meant generating massive streams of information, yet the team lacked a unified engine to make sense of it all in real time.

Challenges

As daily delivery operations scaled, the company’s fragmented infrastructure started creating severe friction across every level of the organization. Operational teams were caught between soaring data volumes they couldn’t process and manual reporting workflows that simply couldn’t keep up with real-time decision-making needs.

• Fragmented enterprise data across Amazon RDS, MongoDB, and Amazon S3 prevented unified analytics and slowed operational reporting — business users had to check multiple systems just to answer basic fleet questions.
• Scale outpacing manual processes — the platform supported approximately 75,000 vehicles, with datasets exceeding 1 TB and growing nearly 20% month over month, a pace that would double the dataset roughly every four months if left unaddressed.
• Manual cross-system lookups for vehicle locations, shipment status, ETAs, and operational reports slowed response times and degraded the customer experience.
• No self-service analytics — business teams remained dependent on manually generated dashboards and reports for day-to-day decisions.
• No predictive intelligence for freight benchmarking or pricing, leaving pricing decisions without a data-driven baseline.

Searce Solution

Our team of solvers designed and implemented a cloud-native intelligent data platform on AWS, combining a centralized Data Lake, Retrieval-Augmented Generation (RAG), conversational analytics, dashboard intelligence, and predictive machine learning into one unified solution — built on Amazon Bedrock, Amazon Kendra, Amazon SageMaker, Amazon Athena, Amazon Q for QuickSight, AWS DMS, Amazon API Gateway, AWS Elastic Beanstalk, and Amazon S3.

• Unified enterprise Data Lake — consolidated Amazon RDS, MongoDB, and existing S3 repositories into a centralized AWS Data Lake, continuously synchronized via AWS DMS with Change Data Capture (CDC), creating a single source of truth for analytics, reporting, ML, and GenAI workloads. Amazon Athena enables serverless querying without burdening operational databases.
• Generative AI-powered fleet intelligence — a RAG-based conversational assistant, built on Amazon Bedrock integrated with Amazon Kendra, retrieves relevant enterprise knowledge before generating context-aware responses grounded in live data rather than static FAQs — covering vehicle location, shipment status, delivery ETAs, delayed consignments, fleet utilization, and supporting logistics documents.
• Conversational business intelligence — Amazon Q for QuickSight lets business users ask questions in natural language and instantly generate dashboards, visualizations, and KPI summaries, with no technical expertise or manually authored reports required.
• AI-powered freight benchmark prediction — a custom Amazon SageMaker pipeline (preprocessing, feature engineering, training, evaluation, managed inference) powers the platform’s first production ML use case: benchmark freight-rate prediction from historical transportation data.
• Built for scale — a cloud-native architecture designed for enterprise logistics workloads and future AI-driven use cases, with AWS Lambda, Amazon CloudWatch, and AWS IAM handling orchestration, observability, and security.

Business Impact

By modernizing its core data architecture and embedding generative AI into daily workflows, the company transformed its operational efficiency and scalability. The modernized platform turned once-siloed fleet data into instant, actionable intelligence for teams across the business.

• Unified 3 fragmented data sources (Amazon RDS, MongoDB, Amazon S3) into a single AWS Data Lake — one source of truth for analytics, ML, and GenAI workloads, replacing manual cross-system lookups entirely.
• Natural-language visibility across ~75,000 connected vehicles, letting customers and operations teams retrieve vehicle locations, shipment status, ETAs, and documents through one conversational interface instead of several disconnected systems.
• Architected to absorb ~20% month-over-month data growth — a pace that would double total data volume roughly every four months — without requiring architectural redesign.
• Removed manual reporting as a bottleneck — Amazon Q for QuickSight lets business users generate dashboards and insights on demand, in natural language, rather than waiting on manually authored reports.
• Stood up the platform’s first production machine learning use case — benchmark freight-rate prediction, designed to a target accuracy within 40% MAE of actual rates — laying the foundation for AI-assisted pricing decisions.
• Delivered one reusable, AWS-native platform spanning data engineering, RAG, conversational analytics, and predictive ML, positioned as the foundation for future supply chain optimization and generative AI use cases rather than a one-off build.

By turning fragmented data into intuitive, conversational intelligence, Searce helped this logistics leader eliminate operational bottlenecks across 75,000 vehicles. The resulting AWS-native foundation not only solves today’s analytics demands but also provides a future-proof springboard for ongoing generative AI innovation.