Many industries, including insurance, are drowning in unstructured data—documents like loss run reports that vary wildly in format and are difficult to analyze. The inability to quickly and accurately extract this information limits decision-making, slows down operations, and adds unnecessary costs.
Solving problems like this requires more than just applying artificial intelligence; it demands clean, reliable data as a foundation. By building tools that extract and structure this data with near-perfect accuracy, companies can unlock powerful insights, automate processes, and make smarter decisions at scale.
InsurTech Amplified sat down with with Scott Knowles, co-founder and CEO of Deep Vector, a company that began by tackling a highly specific problem in insurance—extracting accurate, structured data from messy, inconsistent loss run reports. With over 30 years of industry experience, Scott knew firsthand how much valuable decision-making was hampered by hard-to-access data.
What started as a focused solution for brokers and carriers has since evolved into a powerful platform with potential across multiple industries, from finance to government, all struggling with the same fundamental issue: making sense of unstructured documents.
With recent funding in place and massive expansion opportunities on the horizon, Scott’s story is a brilliant example of how deep industry knowledge, entrepreneurial curiosity, and a people-first mindset can unlock innovations with far-reaching impact.