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Bioletics demo shows iPhone-based AI road-assessment tool; city asked about costs, data and grant use
Summary
A Bioletics presenter demonstrated an iPhone-mounted AI system that photographs roadways to score pavement condition, generate inventories and support budget prioritization; ongoing cost was cited at about $150 per centerline mile and initial setup fees vary by city size.
A presenter from Bioletics Road Management System outlined an AI-based pavement inspection tool that uses an iPhone mounted in a vehicle to capture geo-referenced photos approximately every 12 feet and identify 15 types of roadway distress.
"The way that our costing works is it's based on a total, lane mile or centerline miles across the city," the presenter said, describing an ongoing charge of roughly $150 per centerline mile with an initial setup fee and a customer-success representative for reporting and training. The presenter added that a small installation’s initiation fee might be about $3,500 while larger networks typically see setup fees nearer $9,500.
Council members asked whether the system works on both city and county roads; the presenter said it supports paved, chip-seal and gravel surfaces and can produce a PCI (pavement condition index) score for asphalt. The presenter suggested the system could create work orders, help prioritize repairs, and support grant documentation by providing pre- and post-event photographic records.
Members also discussed practical deployment: the presenter said the device can be mounted in a city vehicle and staff could use the same iPhone for other tasks. Council members pressed on budgeting questions — whether the service would come from regular maintenance funds, capital improvement projects, or cooperative purchasing arrangements — and the presenter said the pricing model allows modules to be included or removed depending on city needs.
The presenter noted limitations and operational advice: although a single pass captures both travel directions in many cases, crews should drive both directions to better capture street signs and asset conditions. The city agreed to follow up with staff to determine possible pilots and budget options.
