TigerEye

TigerEye identified a critical problem plaguing construction sites: critical project information existed across fragmented systems—spreadsheets, emails, photos, and verbal updates—making it nearly impossible for teams to access accurate, real-time data. General contractors and project managers experienced this most acutely, losing hours daily searching for documents, clarifying conflicting information, and making decisions based on outdated details. The problem was measurably observable through project delays, rework costs, and safety incidents directly traceable to communication breakdowns. Existing alternatives—traditional project management software like Procore and Touchplan—required manual data entry and didn't capture on-site reality. Some teams attempted custom solutions using basic databases or shared drives, but these created new silos rather than solving fragmentation. Early validation came through conversations with construction leaders who immediately recognized the pain. When TigerEye demonstrated how AI could automatically extract and organize information from photos and documents already being created on-site, contractors saw immediate relevance. The fact that construction professionals were already taking thousands of daily photos meant the raw data existed—TigerEye simply needed to make it useful without adding workflow burden.

TigerEye launched with a focused MVP targeting daily site reporting—automating photo documentation and progress tracking that construction teams were doing manually. They shipped their first version in under four months, deliberately excluding sophisticated predictive analytics and complex integrations that competitors were pursuing. Instead, they built around a single workflow: capturing images, auto-tagging them, and generating daily reports. This stripped-down approach proved prescient. Early customers immediately adopted the tool because it solved their most painful daily task. Within six months, TigerEye had paying customers across multiple projects, validating that construction teams would pay for time savings on routine documentation. Their constraint—refusing to build features without direct customer requests—forced them to deeply understand their core user's workflow. The execution speed also attracted attention from experienced construction operators who joined as advisors, recognizing the team understood site-level problems rather than theoretical industry gaps. This early validation through actual usage, not surveys, became their strongest signal that they'd identified a genuine market need.