NSV SVL (Small Vector Laboratory) helps large corporations do R&D in today’s new world..

A new approach for R&D is required to help large corporations build big new businesses and innovate. NSV’s SVL program is
designed exactly to solve this problem utilizing a proprietary 3 phase process:


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  • Insights
    • - Insights of new opportunities.
    • - View on emerging trends, technologies and themes.
  • Product/Market Fit
    • - Understanding of key customer need, problem solved.
    • - Product design(informed by venture scanning).
  • Customer/Market Validation
    • - Launch minimum viable product (”MVP”)
    • - Quick iteration to eventual product/market fit.
    • - Quick kill off failures.

Corporate R&D as we traditionally know is dying

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Wrong Culture

Research environments do not have the appropriate incentives and culture to win at all costs.

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Resource Intensive

High cost and long time before market potential can even be determined.

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Low Market Focus

Projects not necessarily selected based upon key consumer problems. Very little or no hypothesis testing.

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Low Visibility

Very difficult to gauge how well projects are going or to understand how well R&D correlates to eventual ROI.

The purpose of R&D is to generate new products for the company…the traditional R&D approach must adapt to today’s new world

At the same time, new venture creation has been evolving

Reduced Resource Costs

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Compared to 5-10 years ago, the costs for developing new software/IT based service has dramatically reduced enabling small teams to create massively scabable products.

Combinatorial Development

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Everything below the application layer is becoming commoditized. Open APIs and modularized software paradigms make new business creation a combinatorial problem of existing technology.

Massive Distribution

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Facebook (over 700M users) and mobile (IOS/Android in the 100Ms) continue to grow rapidly. These platforms have potential to distribute a single product from a small team with a single technology to 100s of millions of users.

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