AI data preparation, model adaptation and human-in-the-loop workflows
Invisible Technologies prepares enterprise data for AI and tailors models to each client, pairing automation with human expertise. Its stack mixes an LLM toolchain (OpenAI, Cohere, Hugging Face, LangChain, Pinecone) with a lakehouse layer (Delta Lake, Unity Catalog, Kafka) across AWS, GCP and Azure. Senior-skewed engineering hiring and recurring pains around messy data and latency point to a firm hardening client-facing AI deployments.
Invisible Technologies is a privately held company headquartered in New York, NY, founded in 2015 and employing 201–500 people. Its platform cleans, labels and structures company data so it can be used for AI, then adapts models to individual businesses with human specialists added where needed. The company also works on workflow automation and operations outsourcing for clients across multiple industries. Engineering runs on Python, TypeScript and React, deployed with Docker, Kubernetes, Terraform, Helm and ArgoCD. Hiring is concentrated in the United States and United Kingdom.
Python, FastAPI, TypeScript and React, with OpenAI, Cohere, Hugging Face, LangChain and Pinecone for AI, Delta Lake and Kafka for data, and Kubernetes, Terraform and ArgoCD across AWS, GCP and Azure.
Invisible Technologies is headquartered in New York, NY, and hires in the United States and United Kingdom.
Job posts point to a next-generation marketplace launch, real-time analytics, AI-driven solutions for client workflows, shared infrastructure components and cloud infrastructure optimization.
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Invisible Technologies'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.