Wand AI builds an enterprise platform where AI agents and human staff share work, with governance and security built in. Its stack pairs Temporal, Kafka and Kubernetes with a broad LLM layer (OpenAI, Anthropic, LangGraph, LiteLLM) and multiple vector stores, pointing to model-agnostic agent orchestration at scale. Hiring is accelerating and staff-level engineering dominates, matching active work on agent memory, orchestration and an AI governance engine.
Wand AI is a Palo Alto, California company founded in 2022 that sells a platform for running mixed teams of human professionals and autonomous AI agents. The product spans agent automation, a shared human-agent workspace, and agents that improve over time, with governance and security controls aimed at enterprise buyers. The privately held company has 51–200 employees and hires across the United States, United Kingdom, India and the Netherlands. Alongside a predominantly engineering team, it is building sales, marketing and support functions to handle enterprise deployments.
Wand AI uses Python, FastAPI, React, Temporal, Kafka, RabbitMQ, Kubernetes and Docker on AWS and Azure, plus OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, and vector stores like Pinecone, Weaviate and pgvector.
Yes. Hiring is accelerating and heavily weighted to engineering, mostly at staff and senior levels, with smaller openings in sales, marketing and support across the US, UK, India and the Netherlands.
Current projects include an agent orchestration backend, an agent memory system, an AI governance engine, LLM-based policy violation detection, a knowledge graph of org structure, and a greenfield product called Wandy.
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Wand AI's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
