With FTTx operators increasingly relying on shared passive infrastructure to build out their networks, Inveniam’s AI helps to ensure compliance with specific quality standards and photographic obligations. Reduce the number of people needed to prepare or review photographic evidence and speed up the sign-off process for both parties, avoiding unnecessary delays and costs for non-compliance, penalties or site revisits.  

Our computer vision solution optimises the asset quality control process at build completion, enabling you to streamline your own site survey checks. Detecting and resolving issues when your engineer is onsite also eliminates rework and network roll-out delays, both of which are vital in the race to win new fibre customers.

Our mission is to empower field agents to resolve faults on the first visit, eliminating the need for an additional site visit or truck roll. Crucial for meeting SLAs, reducing time to repair and ensuring customer satisfaction, our computer vision solution is able to identify potential issues and propose step by step instructions to resolve, particularly helpful for newer engineers that have less experience.  

 Inveniam’s computer vision solution helps ensure every customer install is executed Right First Time (RFT). From fibre splicing to mounting optical network terminals (ONTs), we identify potential anomalies in real time when the engineer is onsite, eliminating the need for an additional customer visit. We help guarantee the best possible customer install and help maximise your net promoters score (NPS) feedback.  

 Inveniam's computer vision solution can support retrospective documentation and auditing of as-built networks. Reviewing photos is resource intensive, often limiting checks to a small sample of the network. Our solution enables you to audit build quality across your entire network footprint, leaving your team to focus on edge case reviews and other priority tasks.  

Through historical data analysis of images and videos, our visual automation solution can be used to monitor the condition of equipment and predict when maintenance is required. By analysing visual data and patterns, we identify when components are likely to fail or degrade. Enabling maintenance to be scheduled proactively, reduces unplanned downtime, fix times and overall customer disruption.