Technical

• Operational data could only be accessed through IT service tickets

• Data extraction was manual, slow, and difficult to scale

• Outputs lacked context and were rarely shaped for decision-making

• Security and infrastructure constraints limited experimentation

Business

• Leaders often made time-sensitive decisions without data

• Front-line and operational teams could not explore data independently

• Access delays reduced trust in analytics and discouraged use

• The organization’s data maturity was constrained by process, not availability

Rather than starting with dashboards or predefined reports, the work focused on how people naturally ask questions.

I anchored discovery in real operational queries, then designed a conversational system that translated intent into structured data access. Architecture, conversation design, and security were developed in parallel, ensuring the solution was both usable and deployable inside a tightly controlled enterprise environment.

This avoided the common failure mode of AI pilots: impressive demos that cannot survive production constraints.

Conversational Data Access

• Natural-language querying of operational data

• Clear confirmations and explanations to support trust and accuracy

• Responses designed to surface insight, not raw tables

Modular AI Architecture

• Specialized agents for query interpretation, validation, execution, and visualization

• Guardrails to prevent unsafe, ambiguous, or misleading outputs

• Structured handling of uncertainty and incomplete data

Hybrid Deployment Model

• On-premises frontend embedded in the existing transportation management system

• Cloud-based AI services for scalable processing and analytics

• Secure data flow through a controlled proxy layer with strict access controls