As energy utilities expand and modernize the grid, teams must verify that a growing number of field installations match their approved engineering designs. That work is essential to grid safety and reliability, but traditional compliance verification can be slow, manual, and vulnerable to human error.
Data Society Group Principal Data Scientist Yekaterina “Katya” Mijatovic will explore a new approach to this challenge at the 4th Data Science & AI Summit during London Data Science Week 2026. Her oral presentation, “From Panel Image to Wiring Compliance: An Explainable Graph-Based Verification Framework,” is part of the Applied Data Science in Industry track.
Cassandra Rounds, an energy utility partner subject matter expert, will co-present the session. The Data Society Group team also developed the research with Ishita Jain, Poornima Joshi, Alexander Berry, and Ethan Gueck.
Moving Beyond Digital Checklists
Current digital tools can help teams manage inspection checklists, but they do not typically compare images of completed field installations directly against engineering schematics. Building that capability requires more than an image-recognition model.
The system must understand the components shown in an installation, how those components are connected, and which standards apply. It must also account for normal variation in field photographs while producing findings engineers can inspect and trust.
The framework Katya and the team developed represents both the approved schematic and the completed installation as attributed graphs built on a shared ontology. The system creates structured representations of the components and connections found in each source, making it possible to compare what was designed with what was built.
A Two-Layer Approach to Compliance
The framework evaluates compliance in two layers. First, deterministic rules check universal installation requirements that should remain consistent across projects.
Next, a severity-weighted graph edit distance compares the field installation with its specific approved design. The resulting distance serves as a compliance score, while the edit path becomes a detailed list of potential discrepancies.
Every finding identifies the affected component, its severity, and the standard governing the requirement. Instead of returning a black-box score, the framework shows users what may be wrong, where the issue appears, and why it matters.
In highly regulated environments, engineers and field teams need enough information to evaluate a finding, apply their expertise, and determine the next step.
Designed for Sensitive Infrastructure
The team demonstrated the framework using medium-voltage underground switching cabinets and evaluated installations against applicable industry and manufacturer specifications. Schematic graphs were automatically extracted from production CAD drawings, while a fixed interface allows a computer vision system to provide structured data from field photographs.
Because the work involved critical energy infrastructure information, all development followed NDA-governed data-handling requirements and remained on local infrastructure, without using cloud services. This let the team build a graph-based model without exposing any sensitive utility data.
Broader Potential for Regulated Industries
Although the initial work focuses on energy infrastructure, the framework can apply to other installations governed by structural rules. Any environment where teams must compare a completed physical system with an approved design could benefit from a similar approach.
Identifying discrepancies during construction, commissioning, or maintenance can reduce costly rework and lower the risk that an overlooked defect becomes an equipment failure or safety incident. The framework’s deterministic and explainable findings could also support future agentic workflows while keeping qualified professionals involved in review and decision-making.
Katya brings extensive experience in scientific computing, machine learning, and applied AI to this work. As a Principal Data Scientist at Data Society Group, she leads technical teams and develops custom solutions and data products designed around the needs of engineers and business stakeholders, with a particular focus on the energy and utilities industry. Learn more about the project here: https://wyrevern.netlify.app/
The 4th Data Science & AI Summit will take place October 1–2, 2026, in London. Explore the full speaker list and event information, or connect with Katya on LinkedIn.
