AI-powered incident correlation for traffic management
A dedicated-team backend and AI engagement supporting Connect Capable's traffic incident management platform. Work covers OpenAI-powered incident correlation, data pipeline refactoring, and the Azure-side infrastructure that runs it.
- Geography
- United States
- Stage
- Growth-stage government tech
- Team
- 1 senior + 1 mid full-stack engineer
- Duration
- Ongoing
The situation
Connect Capable runs an AI-powered traffic incident detection and management platform that government agencies use to coordinate response. The system centralizes reports from Waze, weather monitoring, and manual operator entries into a single incident picture, then classifies and prioritizes for human decision-makers.
The brief covered backend development and AI-system enhancement: improve the data pipeline that ingests heterogeneous sources, expand AI capability with retrieval-augmented generation for context-aware classification, and tighten the Azure infrastructure carrying the platform under government-scale load.
What we built
The engagement is a Dedicated AI Engineering Teams arrangement: a senior full-stack engineer and a mid full-stack engineer embedded with the Connect Capable engineering team on an ongoing basis. AI fluency is a baseline on the team, which mattered here because the work straddles conventional backend engineering and live LLM orchestration.
The AI work centers on a retrieval-augmented generation pipeline powered by OpenAI. Incoming incident reports are correlated against historical context and weather data; classification is context-aware rather than rule-based; and complex incident patterns are visualized for the human operators making response decisions.
On the backend, the team refactored the .NET services carrying high-volume traffic data so the pipeline holds up under load. Data-pipeline work covered transformation, error handling, and orchestration. Azure-side work covered AI Search, storage, scaling, and monitoring so the platform runs reliably as government agencies rely on it.
Tech stack: Microsoft Azure (compute, AI Search, storage, monitoring), OpenAI for the LLM layer, .NET for the backend services, and the data-pipeline tooling that moves information from Waze, weather, and operator-entry sources into the platform.
Outcome
-
OpenAI-powered RAG layer live for incident correlation and context-aware classification
-
.NET backend services refactored to hold high-volume traffic data under load
-
Azure-side scaling, monitoring, and AI Search infrastructure in production
The platform now runs as a more reliable, more scalable, more context-aware traffic incident management surface. The engagement is ongoing on the dedicated-team shape.
"We have monthly goals to complete specific requirements, and Leanware always meets those goals quite easily."
— Jesse Seger , CTO , ConnectCapable · Dearborn, Michigan
Engagement line