An Enterprise Perception System (EPS) is a centralized platform that merges human expertise and AI capabilities to analyze data from various sources, and then to take the appropriate actions in the world. EPS provides a streamlined process for collecting, organizing, interpreting and taking actions upon this data. Whether the information comes from sensors, IoT devices, visual inputs like cameras, or text from online documents, EPS ensures the organisation can handle the volume of data coming into the enterprise and take the best course of action. Just like how the GUI and desktop computers transformed how our organisations worked, EPS works with major technology trends such as low-cost sensors, ubiquitous access to the internet and Artificial Intelligence to transforms how businesses handle and interpret data. It accelerates perception analysis, enabling domain experts to collaborate with AI models to produce precise and timely insights, and enables faster decision making and actions to be taken.
Just like how the GUI and desktop computers transformed how our organisations worked, EPS works with major technology trends such as low-cost sensors, ubiquitous access to the internet and Artificial Intelligence to transforms how businesses handle and interpret data. It accelerates perception analysis, enabling domain experts to collaborate with AI models to produce precise and timely insights, and enables faster decision making and actions to be taken.
Many organizations gather vast amounts of perceptual data from sensors, cameras, IoT devices, and more. But making sense of this data and turning it into actionable insights is often a slow and fragmented process. Teams responsible for interpreting the data can become overwhelmed, leading to delayed decision-making. Whether you’re trying to manage assets, improve workflow efficiency, or drive innovation, relying solely on human analysis or isolated AI models can slow your ability to react to crucial changes within your organisation.
This is where an Enterprise Perception System (EPS) comes in. By integrating human intelligence and Artificial Intelligence (AI) within a centralized platform, EPS allows organizations to analyze and interpret perception data rapidly and efficiently. An EPS not only improves the quality of data assessments but also ensures that businesses can make informed decisions, providing the provenence and context for how the decision was made and the action taken.
With EPS, you can scale your data interpretation efforts across your entire enterprise by decentralizing workflows and empowering human and AI agents to collaborate on the data that matters most.
Enterprise Perception Systems (EPS) evolve through distinct stages of maturity, with each stage reflecting increased standardization, consistency, and optimization in perception workflows. These stages outline the enterprise’s progression from a fragmented, ad-hoc approach to a sophisticated system capable of producing reliable and repeatable outcomes.
At the lowest maturity level (Level 0), organizations operate without consistent processes or standards. Perception workflows are carried out ad-hoc, based on individual intuition or specific needs of the moment. Decision-making at this stage is often reactive, with little to no historical data or standard criteria guiding actions. Consequently, outcomes vary widely, making it difficult to ensure accountability or to replicate successful strategies.
As organizations mature into Level 1, they begin to standardize workflows for collecting and assessing data. Every time a perception task is performed, the same procedures and taxonomies are followed, ensuring consistency in reporting. At this level, decision-making also starts to become more systematic, as actions are taken based on a set of predefined responses tied to specific outcomes. For example, if a particular road defect is identified, the same repair protocol is initiated each time, reducing variability in responses.
Level 2 marks a significant shift, where organizations begin to aggregate and compare data from different workflows. Not only do perception workflows across the organization adhere to standardized taxonomies and processes, but the decisions and actions taken in response to insights become increasingly data-driven. Organizations develop more complex action protocols, allowing them to weigh different courses of action based on comparative analysis. For instance, assessments may now include cost-benefit analyses that guide whether manual inspections or automated LIDAR assessments are more appropriate, depending on the situation.
By the time an organization reaches Level 3, it operates with a highly optimized, feedback-driven system. Not only are perception workflows and assessments standardized across the enterprise, but decision-making and action protocols have matured into dynamic, quantitative systems. Actions are no longer reactive or one-size-fits-all; they are guided by second-order metrics that continuously refine how the organization responds to different scenarios. This might include adjusting maintenance schedules based on long-term cost-effectiveness, or deploying certain agents (human or machine) based on their proven efficiency in similar situations. Actions become proactive, driven by predictive insights and historical performance data, and are regularly updated to improve overall efficiency and accuracy.
Across this journey, decision-making moves from being largely intuitive and disjointed, to a fully systematized and optimized process where decisions are informed by real-time data and iterative improvement. This holistic evolution of both perception and action allows organizations to achieve not only more accurate insights but also better strategic outcomes, maximizing the effectiveness of their resources.
EPS comprises several interconnected subsystems, each designed to optimize data management and interpretation:
Gathers perceptual data from various sources—such as sensors, images, and videos—for processing and analysis. This subsystem ensures that raw data is capture about the world, and funneled into the system quickly and efficiently.
Tracks and organizes important data concepts through a structured hierarchy, ensuring that the most relevant information is easily accessible and interpretable by AI and human agents alike.
Perform data analysis tasks, whether by human experts or automated AI agents.
Once a decision is made, take the appropriate action, whether by human experts or automated AI agents.
Provides the tools necessary for training and deploying both human and machine agents to handle specific tasks.
Continuously monitors the performance of agents, assessing their accuracy, efficiency, and cost-effectiveness.
Produces detailed visual representations of data, such as charts, maps, and reports, helping users to interpret insights and make informed decisions.
