Advancing Trust in AI-Enabled Smart Building Operations

Omniconn helps advance trusted AI for smart building operations

Based in the United Arab Emirates, Omniconn is an IoT platform provider specializing in smart building solutions. Founded in 2020, the company focuses on connecting physical infrastructure — including building management systems, HVAC systems, sensors and fire panels — to support centralized monitoring and control across complex commercial, governmental and mission-critical environments where operational continuity, energy efficiency and life safety are essential.

Omniconn’s AI-powered smart building and operational intelligence platform (software version 4.0) is designed to enable building safety, operational efficiency, sustainability and more autonomous building management. It brings together building management systems, fire and life-safety systems, IoT sensors and devices, operational workflows, and energy optimization capabilities to help organizations manage complex building environments in a unified way — facilitating more efficient energy use and more informed decisions around resource consumption.

Omniconn Platform 4.0 became the first AI-enabled smart building platform globally to achieve UL 3115 AI safety certification.

Managing complexity across connected building environments

In complex commercial campuses, government facilities and other mission-critical environments, operational continuity, energy efficiency and life safety often depend on multiple connected systems working together effectively. These environments are also under increasing pressure to improve energy performance and align with broader sustainability goals, often while managing aging infrastructure and evolving operational demands. However, they are frequently constrained by fragmented building management systems, inconsistent asset configurations and manual processes that do not adapt easily to changing thermal conditions.

Facility teams may have access to large volumes of IoT data, yet still lack clear visibility into how weather conditions, equipment performance, occupancy and building systems interact — making it difficult to spot inefficiencies early and take timely, consistent action as conditions shift.

Omniconn is designed to address these challenges by supporting informed, human‑led decisions while improving visibility into how AI‑assisted recommendations are generated. For example, in a warm‑climate office building, the platform can identify emerging inefficiencies in cooling performance as conditions change. When outdoor temperatures rise earlier than forecast and sun‑exposed zones warm faster than historical patterns, the platform analyzes real‑time inputs from HVAC systems, zone temperature sensors, energy meters and weather data.

Rather than relying on fixed schedules or rules, it flags meaningful deviations from expected operating patterns and presents targeted optimization recommendations — such as adjusting cooling output in specific zones or rebalancing loads across equipment. These adjustments can help reduce unnecessary energy consumption while maintaining occupant comfort and operational stability.

Unlike conventional energy management platforms, Omniconn integrates fire and life-safety systems, building automation, IoT devices, energy optimization and operational workflows to support more coordinated operations across building environments where safety, resilience and efficiency all matter. Recommendations are delivered through a centralized dashboard with supporting context, including which zones are driving increased demand and how current conditions compare with historical baselines. Facility teams remain in control throughout the process. Operators can review, approve, modify or decline recommendations based on comfort, scheduling or operational priorities and then verify system performance as conditions stabilize.

Customer challenge

As AI plays a greater role in building operations, organizations are looking for systems that can respond dynamically while still meeting expectations for security, governance, transparency and human oversight. Stakeholders increasingly want more than stated principles or product claims. They want documented, independently substantiated evidence of how AI behavior is governed, controlled and reviewed over time.

For Omniconn, the decision to pursue certification was not driven solely by a customer requirement. As the platform took on a broader role across connected building systems, including managing energy loads, coordinating fire and life-safety signals and initiating automated operating procedures, the team sought a way, through third-party evaluation, to demonstrate that its AI-enabled functionality was operating safely.

“We had conviction in our platform,” said Saleem Syed, head of architecture and advisory at Omniconn. “But in mission-critical environments, evidence is what matters.”

UL Solutions approach

UL Solutions worked with Omniconn to complete a third-party evaluation to UL 3115, the Outline of Investigation for Safety of AI-Based Products. UL 3115 provides a structured, evidence-based framework for evaluating AI-based products across technical, ethical and governance considerations.

The evaluation approach includes documentation review, process evaluation and targeted technical examination to assess how AI systems are designed, developed, deployed and governed, resulting in documented findings that support an objective certification decision.

For Omniconn, the system-level evaluation examined how the platform’s AI-enabled functionality is designed, governed and controlled when it influences building operations in high-consequence environments. The evaluation moved beyond high-level principles to assess how the platform performs in practice.

Specifically, UL Solutions evaluated whether Omniconn Platform 4.0:
 

Results

Upon successful completion of the UL 3115 evaluation, Omniconn earned AI safety certification for its platform, demonstrating that it met applicable safety requirements. Omniconn Platform 4.0 was the world's first building management platform to achieve this distinction, and Omniconn was the first recipient of AI safety certification from UL Solutions.

With the evaluation complete, Omniconn’s application became eligible to use the widely recognized UL Mark, signaling that safety-related considerations were independently evaluated and helping communicate the company’s approach to responsible AI deployment.

Organizational impact

Beyond the certification outcome, Omniconn describes an internal shift in how the organization approaches AI safety and governance. Prior to the evaluation, AI safety practices were embedded in how teams built the platform. The UL 3115 evaluation process required those practices to be made explicit through documented evidence — clarifying ownership of AI risk decisions, how AI component limitations are documented and communicated to operators, and how governance practices remain consistent and traceable. This work required alignment across architecture, product, engineering and operations and helped establish clearer accountability structures.

That discipline extends beyond a one-time assessment into ongoing product development. Teams that have documented AI limitations and evidence for independent, third-party evaluation may take a more structured approach when designing new features and writing specifications. This organizational learning contributes to a repeatable approach to AI safety, transparency and governance while supporting continued innovation in AI-enabled building capabilities.

As organizations increasingly invest in intelligent infrastructure, this foundation can help position AI-driven platforms within broader digital transformation efforts. By aligning AI safety governance with evolving product capabilities, Omniconn aims to support building environments that are not only more efficient but also more adaptive, resilient and responsive to changing operational conditions.

For procurement and internal review stakeholders, UL 3115 certification can provide a recognized reference point for safety-related considerations such as transparency, security, accountability and human oversight — areas commonly scrutinized during governance and procurement discussions. Referencing a third-party evaluation to UL 3115 can also help translate internal confidence into audit-ready documentation that supports those conversations.