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Deep Vector is an artificial intelligence platform that automates the extraction and structuring of data from unstructured insurance underwriting documents for commercial markets. The enterprise software converts complex analog materials, specifically including loss runs, ACORD forms, and motor vehicle records, into analyzable digital formats to assist commercial insurers and retail brokers with their risk assessment workflows. In January 2025, the software-as-a-service company secured $1.5 million in seed funding through a financing round co-led by institutional investors Aperture Venture Capital and InsurTech NY. The core technology product, which was originally developed and launched under the name Loss Scan, builds directly upon the executive team's prior sector experience, highlighted by the previous sale of the insurance analytics firm Modgic to Zywave. Deep Vector was founded in 2023 by serial entrepreneur Scott Knowles and technical co-founder Wesley Janse van Rensburg.
Deep Vector has raised $2.0M across 1 funding round.
Deep Vector has raised $2.0M in total across 1 funding round.
Deep Vector has raised $2.0M across 1 funding round. Most recently, it raised $2.0M Seed in January 2025.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Jan 1, 2025 | $2M Seed | Aperture Venture Capital, InsurTech NY | — | Announced |
Deep Vector has raised $2.0M in total across 1 funding round.
Deep Vector's investors include Aperture Venture Capital, InsurTech NY.
Deep Vector is an AI-powered platform that automates the extraction, structuring, and analysis of unstructured data from documents like PDFs, images, and scans, primarily targeting insurance but expanding to healthcare, finance, and more.[1][2][4][5] It serves insurers, brokers, risk managers, and businesses with document-heavy workflows, solving the problem of manual processing inefficiencies by using proprietary algorithms and machine learning to handle over 5,600 document formats with high accuracy and speed.[1][3][4][5] The company, previously known as Loss Scan, recently closed a $1.5M seed round co-led by Aperture Venture Capital and InsurTech NY, signaling strong early growth momentum in a data-intensive market.[2][3][5]
Deep Vector was co-founded by Scott Knowles and Wesley Janse van Rensburg, who brought deep expertise from insurance, software development, and startups.[1][3][5] Knowles began as a commercial insurance broker and recently sold their prior venture, Modgic (an insurance analytics business), to Zywave, while CTO Janse van Rensburg specializes in scalable solutions for insurance challenges.[1][3][5] The idea emerged from addressing inefficiencies in handling unstructured data like claims loss runs and ACORD forms; it evolved from Loss Scan's success in insurance data extraction into a broader platform adaptable across industries.[2][3][4][5] Early traction included building a proprietary engine now expanding beyond insurance, culminating in the January 2025 seed funding close.[3][5]
Deep Vector rides the wave of AI-driven document automation amid exploding unstructured data volumes in regulated sectors like insurance, where analog docs hinder risk assessment and decision-making.[3][5] Timing is ideal as insurers face pressure to digitize underwriting—Deep Vector bridges brokers and underwriters by unlocking "buried insights" from PDFs and spreadsheets, improving risk modeling and customer experiences.[3][5] Market forces like insurtech growth, AI adoption in finance/healthcare, and investor interest (e.g., $1.5M seed) favor it, positioning Deep Vector to influence ecosystems by standardizing data extraction and enabling scalable operations across industries.[2][3][5]
Deep Vector is poised for rapid expansion beyond insurance, leveraging its seed funding to adapt its engine for finance, healthcare, and government while showcasing at events like InsurTech Spring Conference.[3][5] Trends like multimodal AI, regulatory demands for data accuracy, and enterprise automation will propel growth, potentially evolving it into a cross-industry standard for unstructured data processing.[1][4][5] As it scales, expect deeper integrations and partnerships, amplifying its role in transforming manual workflows into AI-enhanced intelligence—much like how it revolutionized loss runs, now unlocking broader data potential.[1][3]
