FleetOps AI
Evidence-grounded GenAI for intelligent industrial incident analysis.
An AI-powered incident-analysis platform that transforms industrial telemetry and technical documentation into structured, evidence-backed insights.
- Role
- Software Engineer — System Architecture, Backend & AI
- Year
- 2026
Technologies
Technical context
Industrial equipment generates complex telemetry and diagnostic information. Interpreting these signals alongside technical documentation requires reliable access to relevant evidence.
Problem
Fragmented diagnostic data makes incident investigation time-consuming, while conventional LLM outputs can lack traceability, consistency, and reliable human oversight.
Project overview
FleetOps AI brings together equipment telemetry, diagnostic codes, and technical manuals through a retrieval-augmented AI pipeline. It produces structured incident assessments grounded in relevant evidence rather than relying solely on generative model knowledge.
A controlled workflow manages analysis generation, validation, and human review. The system is designed around modular services, reproducible data pipelines, and cloud-based operations.
Technical approach
- Architected a modular Python/FastAPI backend for industrial incident analysis
- Built a RAG pipeline using semantic embeddings, PostgreSQL, and pgvector
- Orchestrated structured LLM workflows with LangChain and LangGraph
- Implemented evidence validation, uncertainty handling, and auditable human review
- Integrated automated data pipelines, CI/CD, and AWS cloud infrastructure
Technical focus
- Generative AI and retrieval-augmented generation
- LLM orchestration and structured output validation
- Backend architecture and REST API design
- Human-in-the-loop workflows and auditability
- Cloud infrastructure, CI/CD, and observability
Architecture notes
- Modular monolith with separated API, domain, application, and infrastructure layers
- Offline ingestion pipelines decoupled from online incident analysis
- Vector similarity search with PostgreSQL and pgvector
- Bounded LangGraph workflows with validated citations and controlled fallbacks
- Persistent reviewer decisions and audit trails
- AWS infrastructure managed through CloudFormation
Engineering highlights
- Evidence-grounded AI analysis with source citations
- Structured findings with uncertainty and suggested checks
- Human approval workflow with traceable decisions
- Automated ingestion and reproducible retrieval evaluation
- Cloud-oriented architecture with automated quality checks
Outcome
A production-oriented GenAI platform architecture connecting contextual retrieval, structured incident analysis, and auditable human decision-making.