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In wastewater management, the synergy between software, hardware, and comprehensive services are essential components for utilities that aim to understand and optimize their wastewater ecosystem. In an age marked by environmental awareness, urban development and a commitment to resource efficiency, solutions like real-time wastewater intelligence can be a crucial value to achieve such an objective. Wastewater insights can support utilities in improving operational efficiency, workforce management, inflow of the wastewater treatment plant, and its image to attract a younger workforce generation. However, addressing these objectives and ensuring that the solution components work together is a challenging task, as it requires the input of various departments with different objectives, experience, and outputs. In wastewater management, the effective use of software, hardware, and expert services is essential for utilities seeking to understand and optimize their wastewater ecosystem. As utilities face increasing regulatory pressure, environmental responsibility, urban growth, and the need for resource efficiency, real-time wastewater intelligence has become a critical capability. When translated into actionable insight, wastewater data can support improvements in operational efficiency, workforce planning, treatment plant performance, and the ability to attract a new generation of utility professionals.

However, achieving these outcomes is not straightforward. Wastewater intelligence must serve multiple departments including pretreatment, operations, compliance, asset management, and plant operations, each with different objectives, KPIs, and levels of technical expertise. Ensuring that data, analytics, and workflows align across these teams is a significant challenge and requires more than technology alone.

Kando's integration of AI-powered software, along with its specialized hardware and tailored services components, provide utilities a solution component combination that delivers in-depth insights into their wastewater quality that can lead to an optimal running wastewater ecosystem. Kando's API option enables the sharing of wastewater quality insights across different departmental platforms, allowing the data to be utilized utility-wide and support various objectives of different teams. Kando addresses this challenge through its Wastewater Intelligence system, which integrates AI-powered software, specialized hardware, and expert services delivered as Data as a Service (DaaS). At the core of the system are advanced AI and machine learning models that process validated wastewater and industrial data to generate actionable insight. Hardware enables continuous, high-quality data collection, while expert services support deployment, adoption, and alignment across utility teams. Kando’s API further enables wastewater quality insights to be shared across existing utility systems, allowing intelligence to be applied utility-wide in support of different operational and strategic objectives.

This article takes a deep dive into Kando wastewater intelligence solution, a Data as a Service (DaaS) platform, which integrates software, hardware, and expert services components. With a particular emphasis on discussing the software component, it explores how Kando leverages various wastewater and industrial data sets processed by its Artificial Intelligence (AI) and Machine Learning (ML) models to deliver real-time wastewater insights on the dashboard, enabling utilities to move from reactive monitoring to proactive, data-driven wastewater management.

Kando’s wastewater intelligence solution automates the monitoring of wastewater quality and sampling in real time. By continuously analyzing validated data through AI-driven models, the system detects pollution events, identifies deviations and behavioral patterns, and assesses risk across the wastewater network. When high-risk wastewater quality events occur, users are alerted through the dashboard, email, or Telegram, enabling timely awareness and response.

To support daily operations and improve accessibility to insight, Kando has developed STREAMi, an AI-powered chat assistant embedded within the Wastewater Intelligence system. STREAMi enables pretreatment, compliance, and operations teams to access validated, algorithm-generated insights, trends, and sampling information using natural language, simplifying interaction with complex analytics and supporting faster, more confident decision-making. Expert services ensure STREAMi and the broader system are effectively adopted and aligned with utility workflows.

Together, the system enables water utilities to:

Figure 1. Kando’s DaaS dashboard features & STREAMi

Kando wastewater intelligence solution is a comprehensive DaaS (Data as a Service) that seamlessly integrates software, hardware, and expert services, all powered by AI and ML technologies. This integrated approach enables Kando to deliver real-time wastewater intelligence through Kando’s user-friendly dashboard.

Figure 2: Kando Wastewater Intelligence DaaS is composed of Software, Services and Hardware

As depicted in Figure 2, Kando’s Wastewater Intelligence system is delivered as a comprehensive Data as a Service (DaaS) solution that integrates hardware, AI-driven software, and expert services. Together, these components enable utilities to transform raw wastewater data into actionable, system-level intelligence, accessible through Kando’s dashboard and supported by expert guidance.

Software (AI and Analytics)
The software component forms the intelligence layer of the system, processing real-time and historical wastewater data using advanced AI and machine learning models. These algorithms validate data, establish baselines, detect wastewater quality events, classify sources, and generate insights that support operational and strategic decision-making.

Hardware (Data Collection)
The hardware component enables continuous, real-time data collection through the deployment of contactless A-EYE sensors, controllers, and automated samplers. These devices provide the high-quality data required for accurate modeling and insight generation, even in harsh sewer environments.

Expert Services (Adoption and Outcomes)
Expert services support the deployment, operation, and adoption of the system. Wastewater experts, data scientists, and field teams ensure the hardware is installed effectively, the algorithms remain accurate, and insights are aligned with utility workflows and KPIs. Expert services do not generate intelligence independently, but ensure that AI-driven insights are trusted, understood, and applied across departments.

The hardware component captures real-time wastewater data, while the software continuously analyzes and contextualizes it. Together with expert services, this integrated approach ensures utilities can detect wastewater quality issues early, understand their impact across the network, and act proactively.

The system consists of the following attributes:

Kando uses multiple data sets to deliver real-time wastewater intelligence, categorized into four key pillars: Online Data, Utility Data, Historical Data, and Public Data. Together, they generate comprehensive insights on the wastewater quality in the collection system and its regulatory or operational impact.

Online Data, or Real-time data plays a critical role in enabling live insights, providing a snapshot of the current status of the wastewater system and establishes baseline for its wastewater behavior. This data is collected through Kando's IoT devices, the the A-EYE sensor (for more information, please view our hardware article), and processed together with its lab sampling data by a sub-ML model before being analyzed alongside the other data sets discussed below.

Kando's Historical Data consists of an extensive archive built over more than a decade of R&D, which includes identified wastewater fingerprints, key parameters, and industry-specific processes for initial analysis of the type of event it is and its location.

To further improve accuracy, Kando integrates Utility Data to establish benchmark wastewater quality standards, GIS and permits.

Public Data is also utilized to help identify issues and predict future wastewater events.

These four datasets are continuously processed by Kando’s algorithm, providing comprehensive real-time insights into wastewater quality status and flag deviations from its established wastewater baseline behavior, traces sources, calculate the time-window downstream and analyze its impact on the WWTP. Please find below a specific breakdown of each of the sub-datasets utilized.

Figure 3. illustrates the journey data takes through Kando’s wastewater intelligence system, beginning with the integration of various data types including public, utility, online, and historical data.

Before delving into the core functionalities of Kando’s software component and its data acquisition layer model (data value chain showcased in figure 4) of the previously mentioned data sets in chapter 4, it is crucial to understand the foundational technologies that empower this chain: Artificial Intelligence (AI) and Machine Learning (ML).

This section includes a brief overview of AI and ML, emphasizing how they differ yet complement each other in the world of data processing and analysis. Kando’s application of these technologies will be discussed, which is followed by a detailed description of the data flow and the extraction of actionable wastewater insights, facilitated by Kando’s AI and ML algorithms. This will introduce a comprehensive understanding of the integral role played by Kando’s data acquisition layer and how it is processed by Kando's algorithm to deliver real-time and predictive wastewater insights into Kando's dashboard.

Artificial Intelligence (AI) and Machine Learning (ML) are two interrelated technologies that play a crucial role in the development of intelligent systems, but it is important to note that they serve distinct functions within the broader spectrum of data analysis and interpretation.

AI is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. This encompasses a variety of activities, including problem-solving, pattern recognition, and understanding a language. AI aims to create systems that can function intelligently and independently.

ML, on the other hand, is a subset of AI focused specifically on the concept that machines can learn from data, identify patterns, and make decisions with minimal human intervention. ML algorithms allow computers to learn from and make predictions or decisions based on historical data. It is the method by which AI achieves its intelligence, the process through which AI learns.

The key difference lies in their scope and application: AI is the broader concept aimed at mimicking human cognitive functions, while ML is a specific approach within AI that teaches a machine how to learn from data, improving its accuracy over time as it processes more information. AI is about creating intelligence, whereas ML is about the learning process that supports AI's ability to adapt and improve.

To understand Kando's data integration flow that allows the solution to retrieve its insights, we will discuss figure 4 as depicted below displaying the data integration flow and the AI & ML models that are processing the data sets discussed in chapter 4.

The 4 sets of data are fed into Kando's advanced data acquisition system, where its AI model processes the data that captures and streamlines crucial details from the wastewater network ecosystem. This data is then refined by an AI-driven data cleaning process, ensuring it's free from errors and ready for analysis. The process continues with the application of ML models, such as baseline deviation detection, anomaly detection and pattern recognition techniques, to develop a detailed quality event data model. This model monitors live sensor data, recognizes patterns, pinpoints anomalies, and triggers automated sampling.

The value of these initial data insights is further enhanced through algorithmic analysis, leading to enhanced and comprehensive impact insights (discussed in chapter 5.2.1). These findings are then visualized on the Kando dashboard including notification to the user when a certain benchmark has been passed, providing a user-friendly platform for data interaction and decision-making.

To accommodate diverse utility needs and facilitate seamless integration into existing workflows, Kando also offers API access to its data insights, making its solution adaptable and easily incorporated into various wastewater management frameworks of different departments.

Figure 4. Data Flow

In the context of Kando's wastewater data flow, the applications of AI and Machine In the context of Kando's wastewater data flow, the applications of AI and Machine Learning (ML) are central to processing vast volumes of data and deriving actionable insights for its wastewater DaaS solution and API. ML and AI are utilized to tackle the complex challenges of understanding the multiple integrated data sets and allows the software component to analyze, and process wastewater data translated into insights from the complex wastewater network to optimize the network and operations.

Here’s how AI and ML contribute to each stage of Kando's data processing and the calculation of wastewater insights:

At the core of this approach is the baseline methodology. Each collection system develops its own baseline: a learned profile of how multiple parameters normally behave together at a given monitoring point. No single parameter tells the whole story. Each one only captures one type of change. It's the combination that defines "normal," and because that baseline is learned from the site's own validated history, it naturally reflects local conditions, like the upstream industrial mix.

Event detection works by watching for deviation from that baseline. When the combined signal moves outside its normal range, the system flags an event and can trigger an automated sample, without first needing to know which parameter or substance is responsible. Source identification follows: the deviation pattern is compared against known industrial process fingerprints in Kando's historical database, and confirmed matches are fed back into that database, so similar events are recognized faster next time.

Conditions in a collection system shift constantly, so the models are monitored for drift and updated as operating conditions change — keeping every baseline current and detection accurate.

The integration of AI and ML within Kando’s dashboard provides a dynamic and interactive platform for clients to access real-time and predictive insights. ML algorithms drive the dashboard’s capability to present customized alerts, forecasts, and recommendations, in intuitive navigation and visualization of insights, enabling clients to make informed, data-based decisions quickly.

Figure 5. Example of an Event details card widget in Kando’s dashboard

Kando uses a layered data integration and analysis approach to provide actionable insights into wastewater quality events. To break it down, we will take metal coating factory insight detection as an example as shown in figure 5.

As discussed in previous paragraphs, and shown in figure 3, a load detection insight starts with the aggregation of four primary data sets: public data, Kando's extensive historical database (which includes unique fingerprint data), online data and utility-specific information. The latter encompasses GIS data, industry discharge records, factory permits, parameter benchmarks, and real-time wastewater data collected from the network.

This raw data combination is processed through Kando's advanced AI and ML models to transform this raw data into real-time, actionable wastewater insights. Initially, the data undergoes a series of algorithmic treatments, referred to as the data acquisition layer, to cleanse, organize, and commence the correlation process. This stage aims to align the data with specific criteria, facilitating early-stage models to utilize the pre-processed data for initial insights demonstrated in the dashboard insights.
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Kando’s platform is built with robust cybersecurity practices to ensure the protection and integrity of utility data. This includes automated patch management, SSL encryption, secure communication across all components, and strict access controls. The system adheres to industry standards and best practices, aligning with frameworks such as ISO 27001 and SOC 2, and supports GDPR-compliant data handling where applicable. These safeguards ensure that sensitive operational and regulatory data remains protected at every stage, from field sensors to dashboard insights.

Kando's software turns continuous wastewater data into validated, actionable insight. By learning each site's baseline, it detects meaningful changes early, triggers sampling when it matters, and supports source identification through industrial process fingerprints. Validated results are shared through the dashboard, alerts, and API, so pretreatment, operations, and compliance teams work from the same information.

The software does not replace operator expertise. It gives utility teams earlier visibility and clearer evidence, so they can prioritize investigations, protect treatment processes, and make evidence-based decisions across the collection system.

To learn how the other components support this, see our articles on Kando's Hardware and Expert Services, or contact the team at www.kando.eco.