Digitising for efficiency &
speed of smart meter installs
Asset & digital site surveys captured using SmartVideo enabling seamless collaboration & accurate contract mobilisation for a leading electricity and gas distribution company in Asia
Simplify site surveys & improve accuracy of bill-of-materials quote. Complicated disjointed workflows and limited situational awareness in the pre-sales process led to inaccurate bill of material estimates. Inconsistent information capture across sites and meters delayed the customer price quote and meter installation at customer premise. The client’s aim was to build a digital, searchable asset database and improve accuracy of customer price quote and create a more efficient process.
A smart digital platform for richer site intelligence & situational awareness. Vyntelligence simplified & digitised the Electricity, Water and multimeter survey by re-imagining data capture & flow for faster actions.
A rich multimodal guided mobile-first data capture with secure access & offline capability
Two-way collaboration with meter readings, geo-location pins and image annotations for clear contextual information
AI-enabled auto alerts and real-time dashboards to track and remotely assess complexity of meter installs
Downloadable surveyed meter list for seamless process integration with B-O-M creation
Data-driven intelligence & faster, high quality searchable site and asset database for meter installs
“Vyn provides better quality & accuracy of contextual multimedia information captured. We readily share Vyns with contractors and have increased field coverage doing site access & surveys. It helps save about 11.5 man days for a typical 400 meters/ pre-sales site survey”
– Sales Support Manager, Digital Technology
Faster bill-of-materials creation
Visual intelligence and a more seamless real-time flow of job-specific insights has reduced cycle time for BOM creation by 75%.
Optimised Meter replacement
With improved visibility on inventory of electric and water meter brands created the opportunity for a data-driven optimised meter replacement plan