Home Funding Sandgarden Secures $4.5 Million Funding to Accelerate Enterprise AI Adoption

Sandgarden Secures $4.5 Million Funding to Accelerate Enterprise AI Adoption

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Sandgarden, the enterprise AI runtime engine, has secured a $4.5M inception funding round led by Resolute Ventures and Crane Venture Partners, with participation from Panache Ventures, RMS, HearstLab, Locke Mountain Ventures, Jerry Neumann, and other angels.
Sandgarden, the enterprise AI runtime engine, has secured a $4.5M inception funding round led by Resolute Ventures and Crane Venture Partners, with participation from Panache Ventures, RMS, HearstLab, Locke Mountain Ventures, Jerry Neumann, and other angels.
Sandgarden, the enterprise AI runtime engine, has secured a $4.5M inception funding round led by Resolute Ventures and Crane Venture Partners, with participation from Panache Ventures, RMS, HearstLab, Locke Mountain Ventures, Jerry Neumann, and other angels.

Sandgarden, the enterprise AI runtime engine, has secured a $4.5M inception funding round led by Resolute Ventures and Crane Venture Partners, with participation from Panache Ventures, RMS, HearstLab, Locke Mountain Ventures, Jerry Neumann, and other angels.

Read also – Mesa Raises $9.2 Million in Seed Funding

Sandgarden enables enterprises to seamlessly transition from AI experimentation to production, drastically reducing the time and complexity involved in deploying and scaling AI solutions, and ensuring that AI initiatives generate tangible business value fast.

Read also – Centivo Raises $75 Million Equity and Debt Funding

“With Sandgarden, teams are equipped with a modularized, production-ready approach that accelerates their AI initiatives, reduces time to market, and ultimately helps them stay competitive,” said Elizabeth Zalman, CEO of Sandgarden and co-author of Founder vs. Investor. “It is incredibly time-consuming to develop products that leverage large language models (LLMs) or generative AI. We’re empowering businesses to get back to doing what they do best—running their operations—while we handle the complexities of AI integration.”

Enterprises today are under immense pressure to adopt AI initiatives, but the complexity and time required to test, iterate, and implement these solutions leaves many teams stuck in the pilot phase or unable to begin. Engineers face the frustration of not knowing if an experiment will take five minutes or five weeks. Integration is a complicated ordeal, often requiring the development of entirely new infrastructure to support AI initiatives.

Furthermore, AI-specific abstraction frameworks, which are intended to simplify development, often add to the complexity rather than reduce it. Reliability concerns, such as AI hallucinations—where models generate inaccurate or nonsensical outputs—further complicate the adoption process. These challenges mean enterprises cannot rapidly transition AI proof-of-concepts into functional, revenue-generating systems to maintain their competitive edge. Sandgarden’s modularized, production-ready platform addresses these development issues by removing the infrastructure overhead typically associated with AI adoption.

“It’s become a cliché that the big infrastructure providers and LLM companies are spending and building way ahead of the market, while actual enterprise adoption of modern AI is mostly in the kicking-the-tires phase. Those that do manage to run proofs-of-concept then struggle mightily transitioning to production, finding existing products broadly incapable of getting them there,” said Mike Hirshland, Co-Founder and Partner at Resolute Ventures. “These enterprises do not have time to wait for reality to catch up to the promise being sold to us all. The market is ripe for a company to lead the way in bringing that reality forward, and that company is Sandgarden.”

About Sandgarden

Sandgarden is a modularized platform that enables product-driven businesses to rapidly prototype, iterate, and deploy their AI integrations. Founded in 2024 by Elizabeth Zalman, Drew Blas, and Marty McCall, the company is intentionally distributed. C

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