Custom AI-Agent Development for Transport & Logistics Operation Insights

A leading African transportation & logistics enterprise partnered with Intuz for building an AI-powered analytics chatbot—enabling business insights, improved decision-making, and 20+ hours weekly time savings.

A transport and logistics company operating across multiple African regions needed an easier way to analyze large volumes of operational and financial data. Intuz built an AI-powered analytics agent that converts natural language questions into SQL queries, allowing non-technical teams to quickly access insights without IT support.

System Architecture Overview

Problem Statement

Limited Real-Time Data Visibility

Massive datasets—over 500 million operational records—were stored across multiple tables, but data access was slow and fragmented. Managers lacked real-time insights into fleet, finance, and operational KPIs.

Manual Data Querying Bottleneck

Each report request required technical SQL support, consuming up to 15 minutes per query. Non-technical staff struggled to access essential metrics without IT assistance, delaying business-critical insights.

Inefficient Business Analysis

Fuel usage, route profitability, and driver performance analyses required advanced SQL knowledge. Managers couldn’t perform ad-hoc analyses, limiting strategic planning and operational intelligence.

High Time and Resource Costs

Teams spent over 20 hours weekly on data extraction. Manual processes slowed operations, reduced agility, and hindered timely decision-making across departments.

AI-Powered Natural Language Query System

A conversational AI chatbot lets non-technical users access logistics data in plain English. Managers can ask questions about routes, fuel, or drivers and get instant insights. This eliminated SQL dependency and enabled on-demand decision-making, improving data accessibility across operations and finance teams.

Automated SQL Generation with 95%+ Accuracy

Powered by Google Gemini 2.0 Flash, the system converts natural language into accurate SQL queries. It handles simple lookups and complex multi-table analytics with over 95% first-attempt accuracy. Built-in validation ensures each query is safe, logical, and optimized for performance.

Built using Google Gemini 2.0 Flash, Flask, and MySQL, this intelligent chatbot delivers real-time, natural language analytics—empowering logistics teams with instant data visibility, cost efficiency, and decision-making agility.

Problem We Solved

Instant Access to Real-Time Insights

Connected to the company’s MySQL database through a Flask-based API, the solution provides responses in under 2 seconds. Optimized queries and caching deliver instant visibility into fuel usage, delivery performance, and revenue trends—empowering faster, data-driven business decisions.

Secure and Reliable Data Handling

A multi-step validation process checks every query for syntax, safety, and logic using sqlglot. The system runs in read-only mode to prevent unauthorized edits, ensuring enterprise-level data security while maintaining full analytical access for authorized users.

Continuous Learning and Contextual Intelligence

Trained with 300+ lines of logistics-specific context, the AI understands transport terms, metrics, and workflows. It learns from past interactions to improve query accuracy and context awareness—delivering smarter, more relevant insights over time.

Business Impact

The solution saves over 20 hours each week by removing manual SQL work. It delivers complex analytics in under two seconds with 95% accuracy. Non-technical users can access self-service insights, while teams gain better visibility into routes, fuel usage, and driver performance for smarter decisions.

Our AI Agent Development Approach

Discover how our agile AI development process brings your ideas to life, delivering intelligent solutions that drive business growth and innovation.

Data Cleaning and Engineering

The project started by cleaning and preparing over 50 million records for AI use. We removed duplicate data, mapped relationships across tables, and validated joins. This structured data foundation ensured accurate AI training and reliable query results.

Business Context and Domain Intelligence

We trained the AI with 300+ lines of transport and logistics–specific rules. This covered fuel usage, routes, driver performance, and invoicing. As a result, the AI clearly understands industry terms and delivers accurate, relevant, and business-ready responses.

Query Complexity Classification

We built a multi-level query classification system that understands user intent and query complexity. It identifies informational or analytical questions, keeps context across conversations, and labels queries as simple, moderate, or complex, enabling faster, more accurate SQL generation every time.

AI-Based Database Query Generation

Once the system understands a question, it uses Google Gemini 2.0 Flash to generate precise SQL queries. The AI maps user input to the correct tables and columns, validates syntax and logic, and retrieves the data securely in under two seconds. This allows non-technical users to access powerful analytics instantly through natural language input.

Agent Analysis

After retrieving results, the AI formats responses for better readability. It automatically adds context-specific units, currency symbols, and clean labels to every result set. Insights are presented in structured tables, giving operations teams an intuitive and visual way to understand data trends and performance metrics.

Built an Intelligent Learning System

We develop systems that continuously learn from user interactions to improve accuracy and performance. Each successful query-response pair is logged for model refinement, allowing the AI to identify recurring patterns and deliver better results over time. This self-learning mechanism ensures that the analytics agent becomes smarter and more business-aware with ongoing usage.

Technical Challenges We Overcame

During the initial discovery phase and later during the development, we encountered several technical and performance-related challenges that required deep domain understanding and creative engineering solutions.

Converting Natural Language to SQL

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Handling Large Query Results

Many queries returned millions of records, impacting performance. We implemented automatic LIMIT clause injection and result-size monitoring, ensuring 95% of queries completed in under two seconds.

Resolving Column Ambiguity

Similar column names across tables often led to JOIN errors. By enforcing table aliases, documenting relationships, and validating mappings, we reduced such errors from 15% to under 2%.

Managing Multi-Currency and Unit Data

Different currencies and units created inconsistencies in calculations. We built unit-aware formatting and real-time currency conversion rules, bringing unit-related errors below 1%.

API Rate Limiting

Heavy data requests occasionally hit API rate limits. We implemented a smart key rotation system with exponential backoff, maintaining 99%+ uptime even during traffic spikes.