Sandgarden
Modularized AI platform for rapid enterprise deployment.
| Date | Investors | Amount | Round |
|---|---|---|---|
* | $4.5m | Early VC | |
| Total Funding | 000k | ||
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Sandgarden provides a modularized platform, described as an enterprise AI runtime engine, designed to help product-driven businesses to prototype, iterate, and deploy AI integrations. Founded in 2024 by Elizabeth Zalman (CEO), Drew Blas, and Marty McCall (CMO), the New York City-based company aims to reduce the complexity and time required for enterprises to move AI projects from experimentation into production.
The company's core offering addresses the common challenges of AI adoption, such as complex infrastructure development, reliability concerns like AI hallucinations, and the lengthy transition from proof-of-concept to revenue-generating systems. Sandgarden's platform is designed to remove infrastructure overhead, allowing engineering teams to focus on their primary operations rather than the complexities of AI integration. It functions by connecting to a client's existing external systems like CRMs, databases, and knowledge bases to provide context, automate troubleshooting, and deliver suggestions to support engineers with no change to their workflow. The platform also analyzes customer communications to track sentiment and identify potential churn risks.
Sandgarden operates on a business-to-business (B2B) model, targeting enterprises that are under pressure to adopt AI but are hindered by the required resources and complexity. In September 2024, the company announced it had secured a $4.5 million inception funding round. The round was led by Resolute Ventures and Crane Venture Partners, with participation from Panache Ventures, RMS, HearstLab, Locke Mountain Ventures, Jerry Neumann, and other angel investors. An additional seed round in May 2025 brought the company's total funding to $7.52 million.
Keywords: enterprise AI, AI runtime engine, modular AI platform, AI deployment, AI integration, process automation, support engineering, AI adoption, AI infrastructure, LLM integration, generative AI, AI prototyping, CRM integration, sentiment analysis, automated troubleshooting, B2B AI, technology infrastructure, AI daemons, operational efficiency, production AI