Building Intelligent Safety Analytics with Advanced AI Engineering

An automotive manufacturer was struggling to extract meaningful insights from vast amounts of safety-related data scattered across multiple siloed systems. Field reports, warranty claims, consumer complaints, and regulatory documentation existed in both structured databases and unstructured text formats, making it extremely difficult for safety analysts to discover relevant information, identify patterns, and respond to potential safety issues in a timely manner.

The organization needed a solution that could effectively retrieve information from diverse data sources, understand automotive safety domain-specific terminology, and provide intelligent search capabilities that would help analysts quickly identify relevant signals and patterns. Without a unified, intelligent system, critical safety information was being missed, investigations were delayed, and compliance risks were increasing.

The challenge: making sense of fragmented safety data

Safety analysts were spending excessive time manually searching through multiple systems, trying to piece together information from different sources. The lack of standardized data models meant that similar information was stored differently across systems, making it nearly impossible to get a comprehensive view. The organization needed a solution that could not only search across all data sources but also understand the context and relationships within automotive safety data.

"Our safety analysts were drowning in data but couldn't find the information they needed when they needed it. We had multiple systems with overlapping but inconsistent data, and our search capabilities were limited to basic keyword matching. We needed AI that could understand our domain and help us discover insights we didn't even know to look for."

The manufacturer's goal was clear: build an intelligent safety analytics platform that could unify data from multiple sources, understand automotive safety domain concepts, and enable fast, accurate discovery of relevant safety information—all while maintaining the highest standards for operational reliability and scalability.

DC Group partnered with the automotive manufacturer to develop a comprehensive Safety Intelligence platform leveraging advanced AI engineering techniques. The solution combines domain-specific knowledge, state-of-the-art retrieval augmented generation (RAG), and intelligent search capabilities to transform how safety analysts discover and analyze critical information.

Domain-Specific Taxonomy and Knowledge Engineering: We developed a comprehensive automotive industry and safety domain-specific taxonomy covering failure modes and safety categories. This taxonomy enables the system to recognize relationships between different safety data types and improves retrieval accuracy and contextual understanding.

Advanced RAG Implementation: We implemented scalable RAG pipelines with sophisticated ingestion for structured and unstructured data, intelligent chunking strategies, hyperparameter tuning, comprehensive evaluation frameworks, and full observability for production monitoring.

Hybrid Search Architecture: We combined semantic vector search with keyword-based retrieval to handle both conceptual queries and specific technical searches, significantly improving retrieval accuracy across diverse query types. Sub-second response times and high recall rates for domain-specific queries are achieved by using the latest advancements in AI and ML techniques.

Model Fine-Tuning for Automotive Domain: We fine-tuned proprietary and open-source models on automotive safety terminology and failure patterns, resulting in models that understand domain context far better than general-purpose models. All while reducing the inference time and cost of the models.

"DC Group's approach to AI engineering was exactly what we needed. They didn't just deploy off-the-shelf solutions—they built a system that truly understands our domain. The combination of domain-specific taxonomy, fine-tuned models, and hybrid search means our analysts can find information in minutes that used to take hours or days. The data pipelines are automated, reliable and scalable."

Robust data engineering and infrastructure

Unified Data Architecture: We built a standardized data model unifying Snowflake for data warehousing, AWS S3 for document storage, and Milvus vector database for semantic search. Prefect pipelines orchestrate data flow, eliminating silos and providing a single source of truth.

Scalable Platform Architecture: Built on Python with Pandas and NumPy for data processing, FastAPI backend, and React frontend. The cloud-native architecture scales horizontally on AWS with automated scaling and monitoring.

Operationalization and continuous value delivery

Production Deployment and Monitoring: We operationalized the system with CI/CD pipelines, comprehensive monitoring, automated testing, and built-in observability tracking retrieval accuracy, response times, and user satisfaction metrics.

Ongoing Support and Enhancement: DC Group provides continuous support, regularly retraining models, updating taxonomies, and optimizing performance based on usage patterns to ensure long-term value delivery.

The Safety Intelligence platform has fundamentally transformed how the automotive manufacturer approaches safety data analysis. Within the first six months of deployment, safety analysts reported a 70% reduction in the time required to discover and analyze relevant safety information. What previously took hours of manual searching across multiple systems now happens in minutes through intelligent, context-aware search.

Reduced Time for Discovery and Analysis: The combination of domain-specific taxonomy, fine-tuned models, and hybrid search enables analysts to quickly identify relevant signals and patterns. The system understands context and relationships, helping discover connections between seemingly unrelated reports. The system provided ways to tag and label data records which enhanced the collaboration and communication between safety analysts and field staff further improving the upstream data quality and accuracy.

Standardized Data Models Without Siloed Systems: The unified data architecture eliminates silos, providing a single, standardized view of safety information. This improves data quality, reduces inconsistencies, and enables more accurate analysis while making it easier to integrate new data sources. The system improved overall data governance and established a data-driven culture within the organization.

Decreased Operational Spend: Consolidating multiple systems into a unified platform reduces infrastructure costs. The cloud-native architecture scales efficiently, and automated data pipelines eliminate manual processing costs.

Resilient and Scalable Systems: The platform handles significant increases in data volume and user load without performance degradation. Automated monitoring ensures high availability, and the cloud-native architecture enables rapid scaling.

Continuous value through ongoing partnership

DC Group continues to work closely with the manufacturer to enhance the platform, incorporating new data sources, refining models based on usage patterns, and adding new capabilities as requirements evolve. This ongoing partnership ensures that the platform continues to deliver increasing value over time, adapting to changing needs and incorporating the latest advances in AI and Data engineering.