Bottlenecks in Energy Data Processing
ClevAir delivers intelligent systems that help organizations manage building energy consumption, automate maintenance operations, and improve climate efficiency through data-driven operational insights.
As the organization onboarded more clients, the company faced growing operational challenges in preparing and standardizing large volumes of incoming data.
The organization struggled to scale manual data preparation processes efficiently across increasingly diverse client environments.
- Manual data labeling and cleaning workflows: Incoming datasets required extensive manual preparation before optimization and analytics workflows could begin.
- Highly heterogeneous source data: Client datasets varied significantly in structure, formatting, and semantic meaning, making standardization difficult across onboarding processes.
- Slow onboarding and insight generation: Data preparation delays slowed customer onboarding and postponed the delivery of actionable business insights.
- Limited automation of existing workflows: Repeating manual classification and transformation processes for every new client created operational inefficiencies.
An intelligent data classification platform was required to automate data preparation workflows, improve operational scalability, and accelerate customer onboarding operations.
Developing an AI-Powered Data Classification Platform
To address these challenges, ClevAir needed a platform capable of understanding semantic data structures, identifying data types automatically, and applying intelligent transformation logic at scale was essential.
Our team engaged as a strategic engineering partner to design and implement a scalable AI-powered data classification solution tailored to energy technology operations.
1. Building an Automated Semantic Data Type Detection Engine
Addepto developed a machine learning–based system capable of automatically identifying the semantic type of each data point across highly diverse datasets.
Instead of relying on manually defined rules, the model was trained on real-world semantic examples to recognize contextual patterns such as dates, locations, and numerical values more intelligently.
Automated semantic detection improved classification accuracy while significantly reducing the need for manual data labeling and preparation.
2. Designing a Model Resilient to Dirty and Unstructured Data
The platform was engineered to handle noisy, inconsistent, and partially incomplete datasets commonly generated by smart building systems and IoT sensors.
A robust data classification model enabled reliable processing across real-world operational environments where formatting inconsistencies and missing values are common.
3. Replacing Traditional Rule-Based Systems with Predictive AI Models
Traditional decision tree approaches and custom rule-based systems were evaluated during the development phase but ultimately proved too limited in predictive accuracy and long-term scalability.
The selected machine learning model delivered stronger classification performance while improving adaptability across new datasets and evolving client environments.
4. Building a Scalable AWS-Ready Deployment Architecture
The solution was implemented and tested within an AWS-based environment to ensure cloud readiness, scalability, and seamless deployment across customer systems.
A cloud-native deployment architecture enabled ClevAir to integrate the platform efficiently into onboarding workflows while supporting future operational growth across additional client environments.
“Our newly developed system enables all the microservices required for PDF generation to operate in parallel, dramatically reducing the time needed to generate large batches of PDFs while improving scalability and resilience.”
Piotr Danielczyk
Senior Data Engineer – Addepto
Strengthen Data Operations with Advanced Intelligence
The partnership between Addepto and ClevAir transformed manual and time-consuming data preparation workflows into a scalable classification system. Operational teams can now process incoming datasets more efficiently while reducing manual intervention across onboarding operations.
By automating semantic data detection and transformation, the platform also accelerated the delivery of operational insights, enabling new customers to understand the value of the system much earlier in the onboarding process.
Before
- Manual data cleaning and labeling workflows
- Slow onboarding caused by repetitive preparation tasks
- Difficulty processing heterogeneous datasets consistently
- Limited scalability across growing client operations
After
- Automatic semantic data classification
- Faster and more automated onboarding workflows
- Improved consistency across heterogeneous source data
- Scalable data preparation operations with reduced manual effort
The intelligent automation workflows also strengthened operational efficiency by enabling teams to focus more on optimization and analytics initiatives rather than repetitive data preparation activities.
As part of KMS Technology, Addepto continues to help organizations modernize operational workflows through scalable AI platforms, intelligent data engineering, and automation-first solutions.
Ready to strengthen data operations with AI-powered automation? Contact us today!