IQGeo’s planned acquisition of Deepomatic will redefine network lifecycle management for fiber and utility businesses, helping operators deploy, connect and repair networks faster with AI.
by David Cottingham, CTO of IQGeo, & Aloïs Brunel, Co-Founder and CPO of Deepomatic
Many broadband and electric utility operators continually struggle to maintain an accurate overview of their networks. Ensuring all deployments and repairs to infrastructure that spans cities, regions and countries are correctly recorded is an enormous task, especially as pressure mounts to be the first to establish urban fiber networks and the race to achieve net zero emissions by increasing electrical grid capacity heats up.
Deepomatic and IQGeo are addressing this challenge with AI-powered computer vision technology deployed across the network lifecycle. The combined solution automates network field operations, ensuring tasks are completed correctly and network data is accurate. Over time, operators will be able to build on this foundation to create a predictive, proactive network model that identifies and rectifies issues before they cause problems.
Our two companies’ shared vision to redefine network lifecycle management will unfold across three chapters:
Chapter one: Ensuring field work is done ‘right first time’
The stereotype of a visit by a utility or telecom company always requiring a second appointment has a grain of truth. For many companies, double-digit percentages of ‘truck rolls’ end in failure, in great part because work is either not completed to the required standard or documented incorrectly. When a single visit can cost operators the same as a single customer pays for a 6-month subscription, reducing the need for repeat visits is of strategic importance.
Deepomatic’s AI computer vision software is changing the game for network operators by ensuring field work is executed ‘right first time.’ By instantly analyzing images taken by field engineers of network assets, Deepomatic identifies errors and offers guidance on how to fix them. Automating quality control in this way removes the guesswork from tasks like service activations, asset inspections and damage assessments to ensure they are all completed to the same high standard.
Combined with IQGeo’s workflow management software, field workers can only mark a task as complete once the AI confirms that the job has been executed correctly. This completely removes subjectivity from workflows and prevents errors going undetected. In the utilities sector, where field workers often encounter electricity hazards, the combined solution can also block task progress until safety concerns like incorrect wiring are addressed. It also means that contractors can be paid upon task completion instead of having to wait for an inspection.
Adding artificial intelligence to quality control helps companies at all stages of the network management lifecycle. For companies constructing new networks, it means they can build their networks with a quality-by-design approach, while companies in the later stages of infrastructure rollout can increase the precision of high-volume operations like customer broadband connections and meter reading visits.
Chapter two: Establishing a network data quality standard
It is an open secret that most operators’ data quality is poor. In some instances, network data is missing entirely. In densely populated areas like cities, operators do not always even know whether network cables are above or below ground. They often either rely on third-party data sources such as Google Maps or send out teams to manually check the location (or existence) of network infrastructure and verify the network environment prior to deployments. Documenting and maintaining data in this way is simply not sustainable.
Capturing high-quality network asset data is also only one part of the picture. Operators must also correctly document this data in a standardized manner in the network’s digital twin each time they execute new field work. Otherwise, what was once perfectly accurate data will degrade over time as new maintenance and expansion activities are carried out. AI is not only essential to improving data quality but ensuring that this data quality is maintained over time.
Deepomatic and IQGeo are helping companies both maintain and enhance network data quality. Together with its metadata, each photo taken by field workers acts as a snapshot of the state of network infrastructure at the point it was last visited, showing which cables are plugged into which ports in a cabinet, the exact location of utility boxes, etc. Crucially, these datapoints are uploaded to the network management system in near real time, highlighting differences between what is in the field vs. the system of record so that the latter can be updated. By actively correcting existing network data, IQGeo’s and Deepomatic’s combined solution allows operators to make more informed decisions and fix faults more quickly.
Chapter three: Building predictive and proactive network models
Adding AI-powered task management to IQGeo’s workflow solution marks an important step in achieving our vision of a predictive and proactive network model. The more images operators collect of their network assets to document the type of issues that occur, the more AI can identify patterns that they can use to predict and prevent critical network events.
Operators will have the insights they need to make more informed decisions when it comes to network maintenance. Rather than inefficiently replacing assets based on policy, operators can make upgrades when the model identifies the need. The same goes for network expansion as operators start to notice trends in network demand and can then make data-driven investment choices.
As their network models mature over time, utilities and fiber companies will also start to close the loop between analysis and action. In addition to predicting impending issues, AI-powered task management will problem-solve for itself and generate tasks for field workers to complete. This will involve ensuring tickets are created for the right crews at the right time with the tools, training and certifications required to resolve the issue, significantly shortening repair times while ensuring tasks are completed right the first time.
In the long term, these models will become so advanced that physical networks will start to speak for themselves. With AI, network assets and the broader environment can work together to self-monitor and make proactive decisions on the best course of action with minimal human input. Making the network self-sufficient will revolutionize quality control, predictive maintenance and cost optimization — realizing our vision of helping to build and maintain better networks.
Contact us to find out about how IQGeo and Deepomatic can help your business increase fieldwork accuracy and efficiency while enriching digital twins with unprecedented network data.
