How I Would Build an AI Assistant for Real-Time Shipment Visibility
A practical guide to building a grounded AI assistant for shipment visibility using trusted logistics data, controlled queries, and human oversight.
The speaker argues that while advanced logistics technologies like autonomous trucks and automated warehouses garner attention, most teams struggle with a simpler issue: the existing data is scattered across multiple systems, making it difficult to quickly find answers to common questions about shipments. For example, locating a shipment's current status often requires searching through various tables, filtering data, and interpreting different records.
To address this challenge, the speaker worked on a shipment-visibility application that aimed to provide immediate answers through a conversational AI assistant. The assistant would answer only approved logistics questions and show evidence supporting each response, rather than the more general-purpose chatbot approach.
The application needed to retrieve specific information such as current shipment status, last event, recent depot, pickup and delivery locations, update history, and shipment journey. While a traditional dashboard could display all this data, it would still burden users with knowing how to interpret the information. The conversational assistant could simplify this by allowing users to ask direct questions in natural language.
However, the speaker stresses that the AI should not be responsible for generating the shipment facts themselves. Instead, the AI should receive pre-curated, approved queries that pull data from the enterprise systems. The application first understands the user's request, identifies relevant data, runs a verified database query, retrieves the necessary records, and then hands those off to the language model which summarizes the relevant information.
To ensure reliability, the system cleans the shipment timeline by removing duplicate or near-duplicate status records and orders the events chronologically. Each timeline record includes only key fields like status code, description, timestamps, depot codes, locations, and the source system. By limiting the AI's capabilities to specific tools that query data according to predefined rules, the speaker argues the system can provide accurate, evidence-based answers while preventing the AI from making unsupported claims.
Written by urgent.news from HackerNoon's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
