Agentic AI Consulting & Development

A conversational AI agent service powered by LangGraph and GraphRAG that analyzes large-scale water distribution network data through natural-language queries and detects anomalies.

Autonomous Intelligent Analysis

Based on each user query, the AI autonomously selects from 13 specialized tools to analyze time-series and graph data

Integrated Data Architecture

Combines more than 70 million time-series records in MariaDB with complex network structures in Neo4j for connected, context-aware analysis

Semantic Mapping

Vector embeddings map everyday language to precise locations within the water network and identify similar or related asset names

Continuously Updated Data

Automated daily ETL processes synchronize source data and regenerate vector embeddings whenever changes are detected

Diagram of water network connectivity relationships

Service

Service Features

01

Conversational Network Analysis

Automatically explores network graphs and analyzes time-series trends using natural-language queries

02

Specialized Analysis Tools

Provides Minimum Night Pressure (MNP) trend analysis, block-level performance analysis, and anomaly detection

03

Autonomous Workflow Orchestration

The AI agent handles the entire process—from understanding user intent and selecting tools to retrieving data, performing calculations, and generating results

04

Intelligent Visual Reporting

Instantly presents analysis results as map-based heatmaps, MNP charts, block comparison charts, and concise summaries

05

Complex Relationship Querying

Handles relationship-based queries such as, “Show the pressure trends for all monitoring points within a specific block.”

Features

Key Service Benefits

Icon: user-friendly interface for non-experts
User-Friendly Interface for Non-Experts
Enables users to access advanced data analysis through natural-language conversations—without writing complex SQL or queries
Icon: Neo4j-based network intelligence
Neo4j-Based Network Intelligence
Goes beyond simple table searches by mapping relationships among monitoring points, blocks, customers, and service areas as a connected graph
Icon: reliable, context-aware analysis
Reliable, Context-Aware Analysis
Combines vector-based semantic search with graph traversal to improve data accuracy and provide deeper contextual understanding
Icon: optimized for large-scale data
Optimized for Large-Scale Data
Processes more than 70 million time-series records through a high-performance analytics engine with minimal delay

Architecture

System Architecture

System architecture diagram

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