RAG Development for a Government Traffic Platform: Connect Capable
Shipverra's dedicated AI engineering team built the RAG pipeline behind ConnectCapable's government traffic platform, correlating incident reports on Azure AI Search and OpenAI at production scale.
- Geography
- United States
- Stage
- Growth-stage government tech
- Team
- 1 senior + 1 mid full-stack engineer
- Duration
- Ongoing
The situation
What does a RAG development company actually build when the client is a government agency? For Connect Capable, it meant a retrieval-augmented generation pipeline that correlates incident reports from Waze, weather monitoring, and manual operator entries into a single picture, then classifies and prioritizes each one for the humans making the call, a pattern of AI in traffic management now running in production.
The brief covered backend development and AI-system enhancement together: improve the data pipeline that ingests those 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 runs as a dedicated AI engineering team: a senior full-stack engineer and a mid full-stack engineer embedded with Connect Capable's own engineering team on an ongoing basis. AI fluency is a baseline on the team, which matters here because the work sits between conventional backend engineering and live LLM orchestration.
The RAG pipeline is powered by OpenAI. Incoming incident reports are correlated against historical context and weather data, classification runs context-aware rather than rule-based, and complex incident patterns get 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, covering transformation, error handling, and orchestration across the pipeline. On the Azure side, the team's work covers AI Search, storage, scaling, and monitoring so the platform stays reliable as government agencies depend on it day to day.
"We have monthly goals to complete specific requirements, and Shipverra always meets those objectives quite easily." Tech stack: Microsoft Azure (compute, AI Search, storage, monitoring), OpenAI for the LLM layer, .NET for the backend services, and the pipeline tooling that moves data in from Waze, weather, and operator-entry sources.
Outcome
The OpenAI-powered RAG layer is live for incident correlation and context-aware classification, the .NET backend and the Azure-side scaling, monitoring, and AI Search infrastructure are in production. The engagement continues on the same dedicated-team shape, with the embedded engineers extending both the AI layer and the backend as Connect Capable's platform grows.
"We have monthly goals to complete specific requirements, and Shipverra always meets those goals quite easily."
Engagement line
Engagement FAQ
What does a RAG development company deliver on a project like this?
A dedicated team that builds the full retrieval-augmented generation pipeline, retrieval layer, LLM orchestration, and the classification logic on top, rather than just an API wrapper around a model. For Connect Capable, that's an OpenAI-powered RAG pipeline correlating incident reports against historical and weather context.
How does RAG work on top of Azure AI Search?
Azure AI Search handles the retrieval layer, indexing and surfacing the historical and contextual records the LLM needs before it classifies a new incident, while OpenAI handles the generation and reasoning on top of what's retrieved. Connect Capable runs both together in production.
What team does a production RAG build require?
For Connect Capable it is two embedded engineers, a senior and a mid full-stack engineer, with AI fluency built into the team rather than treated as a specialist add-on, since the work spans conventional backend engineering and live LLM orchestration on the same platform.
Can a .NET backend hold up under high-volume data feeding a RAG pipeline?
Yes, with the right refactoring: transformation, error handling, and orchestration work tuned specifically for the ingestion load. Connect Capable's engineering team refactored its .NET services for exactly this.
What does AI in traffic management look like in production?
It pulls incident reports from multiple sources (crowd-sourced data like Waze, weather feeds, manual operator entries) into one picture, then uses a RAG pipeline to correlate and prioritize them for human decision-makers instead of leaving each source siloed.