From Survey to Structural Analysis In a Fraction of the Time
New to the Looq Platform, qPole helps you transform simple field captures into accurate PLA-ready pole models — delivering engineering-ready asset models in a fraction of the time and cost compared with traditional workflows.
Today, you spend ~30 minutes per pole with manual inconsistent field processes and back-office data that can lead to poor repeatability, late error discovery, and high rework. QA/QC occurs downstream, requiring engineers and designers to spend more time validating inputs instead of performing meaningful analysis. With qPole, the AI-enabled workflow allows you to quickly capture and model poles while focusing on engineering decisions rather than data cleanup. Now you can avoid both refielding and overbuilds.
Build PLA-Ready Pole Models in Half the Time
With this new workflow you will spend ~2 minutes capturing each pole – this is significantly less time than you currently spend capturing poles. Additionally, the AI-enabled processing and easy to use web application environment means you will spend less time (~5 minutes per pole) in the back office. Extract all structured geometry and component attributes before pole load analysis begins.
Then, export your PLA-ready data to common modeling sofware such as SPIDAcalc, PLS-CADD, and O-Calc.
qPole provides a standardized workflow with a savings of ~23 minutes per pole and shifts engineering validation upstream—before PLA.
You get an accurate model quicker and at a fraction of the time and cost. The accuracy helps to avoid overbuilds and derive correct construction requirements.
Join the 2026 qPole Early Access Program
A select set of customers will be invited to participate in our 2026 qPole launch, a solution designed to transform how utilities and their service providers collect and create pole models.
The qPole roadmap demonstrates significant time savings across three stages. Currently in Stage 1, qPole delivers pole modeling in approximately 13 minutes per pole — compared to 30 minutes with traditional workflows. As a result, teams save an average of 23 minutes per pole today.
With planned AI enhancements in Stage 2, modeling time reduces further to 5-10 minutes per pole.
At General Availability, the target is 3-5 minutes per pole — representing up to a 10x improvement over traditional methods.
Submit the form below to be considered.
qPole Frequently Asked Questions
How does qPole differ from traditional pole data collection?
Traditional pole data collection relies on manual field processes that are inconsistent and time-consuming. QA/QC occurs downstream, after errors have already propagated. qPole standardizes the workflow and shifts validation upstream before PLA begins. As a result, total modeling time reduces from approximately 30 minutes to under 13 minutes per pole today.
Is qPole available now?
qPole is currently in early access. A select group of customers are being invited to participate in the 2026 qPole launch program. Organizations interested in early access can apply through the Looq AI website.
How does the Looq Platform compare to other pole data collection Entry-level tools use off-the-shelf hardware with a single uncalibrated camera. They rely on simple 2D photogrammetry with no supporting sensors and no point clouds. They lack accuracy refining algorithms and in-span geometry, resulting in inconsistent accuracy. A 2-person crew is required, capturing 100-150 poles per day, with high potential for fielding association errors.
Mid-tier tools use purpose-built hardware with a single calibrated camera. Supporting sensors include laser range finders, IMU, and GPS. AI-assisted annotation is available. However, they still rely on 2D photogrammetry without point clouds, in-span geometry, or accuracy refining algorithms. Accuracy remains inconsistent with high potential for fielding errors from photos or magnetic interference. A 1-person crew captures 50-75 poles per day.
The Looq Platform uses purpose-built hardware with four calibrated cameras in a panoramic configuration. Supporting sensors include IMU and GPS. Outputs include full 3D point clouds from sensor-assisted 3D photogrammetry. Accuracy refining algorithms, in-span geometry, consistent accuracy, and AI-assisted annotation are all included. A 1-person crew captures 150-200 poles per day with low potential for fielding errors.
