Industrial Incident Analysis

The Challenge

Our customer was looking to support its clients in transforming the traditionally manual, inconsistent, and error-prone process of collecting witness statements into a structured, AI-guided workflow. The objective was to enable faster, more accurate AI-assisted incident investigation while reducing the likelihood of similar incidents.

Solution

To meet the customer requirement, an enterprise-grade product was designed and built to enable features such as incident intake, timeline creation/ validation, corrective action recommendation, and compliance report generation. The core components of the solution included:
  • Scalable module for Data Ingestion
  • Reactive mobile interface to collect investigation data
  • Base functionality for conducting Root-cause-Analysis (RCA) and providing recommendations
The application was built with a frontend tied to a backend running serverless AI workflows, ensuring scalability and cost efficiency. Data was managed across relational, unstructured, and graph stores, giving flexibility for transactional, media, and relationship‑driven workloads. Sensitive information was protected through PII masking, pseudonymization, and strict handling across storage, logs, and exports, ensuring compliance from the ground up. The development velocity was enhanced with AI‑assisted coding tools. Observability, Automation, and infrastructure were fully integrated into DevOps pipeline with monitoring, secrets management, and infrastructure‑as‑code and enforce role‑based access.

Outcomes

The solution turned investigation data into early warning intelligence and slashed the data collection/ analysis overhead while capturing critical insights trapped in unstructured data. It has been successfully adopted by large industrial enterprises in the US.