Custom AI-Agent Development for Transport & Logistics Operation Insights
Intuz Development & Consulting
- Data Cleaning and Engineering
- Business Context and Domain Intelligence
- Query Complexity Classification
- AI-Based Database Query Generation
- Agent Analysis
- Built an Intelligent Learning System
About the Project
A large-scale transport and logistics company operating across multiple African regions needed a smarter way to analyze its massive database of fleet operations, fuel records, route performance, and financial transactions. Intuz built an AI-powered data analytics agent that translates natural language questions into SQL queries, helping non-technical users instantly access insights without relying on IT teams.
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.
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.
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
- 20+ hours saved weekly by eliminating manual SQL querying.
- < 2-second response time for complex analytical requests.
- 95% query accuracy, enabling reliable decision-making.
- Self-service analytics for non-technical users, improving agility.
- Enhanced operational visibility into routes, fuel, and driver performance.
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 began with preparing over 50 million operational records for AI processing. Our team implemented automated scripts to remove duplicate data across journeys, expenses, and deliveries. Relationships among multiple tables were carefully mapped and validated to ensure precise joins and analytics. This clean, well-structured schema later formed the foundation for accurate AI training and query generation.
Business Context and Domain Intelligence
To help the AI understand the transport and logistics domain, we embedded over 300 lines of business-specific intelligence into the model. This included logic around fuel allocation, route performance, driver efficiency, and invoicing processes. By adding this domain knowledge, the system was able to interpret business terminology accurately and generate contextually meaningful responses.
Query Complexity Classification
We designed a multi-level query classification layer that identifies the intent and complexity of each user request. The AI detects whether a query is informational or analytical, manages multi-turn conversations by retaining context, and categorizes questions as simple, moderate, or complex. This structured approach ensures faster and 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
We help health systems streamline diagnostic workflows, surface actionable insights from patient data, and reduce manual admin by applying AI in ways that support clinical teams without compromising compliance or trust.
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.
Tools & Technologies That We Use
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Tailwind CSS
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