Does Context Data support on-prem deployment?

Yes, the page lists self-hosted and on-premises options alongside SOC 2 compliant cloud.

What is included beyond the RAG framework?

The Sapphire data engineering layer handles connecting and formatting data from internal sources before retrieval.

Is it certified for security?

The page lists SOC 2 Type I and Type II certifications and notes encryption in transit and at rest.

Do I need to write code to use it?

The product is described as a no-code connectivity framework, though it is targeted at developers and enterprises.

Context Data: Data Processing & ETL infrastructure for Generative AI applications

What is Context Data?

Context Data is a data infrastructure platform for building retrieval-augmented generation and other generative AI applications. It connects internal data sources to embedding models and vector databases through a no-code framework, and offers cloud, self-hosted, and on-prem deployments. The site is positioned for SMBs that want enterprise-grade RAG without building the pipeline from scratch.

Why Context Data works

Standing up a RAG pipeline normally means stitching together connectors, chunkers, embedding jobs, and a vector store yourself, which is why prototypes stall before production. Context Data ships that stack as a managed framework with scheduled refreshes, so engineering teams skip the plumbing and focus on the application around the retrieval layer.

Context Data features

RAG server framework. Deploy a private RAG query layer with no custom coding so a team can spin up a private ChatGPT-style assistant quickly.
Sapphire data engineering. Connects multiple internal data sources and formats the data for AI use so engineers skip writing custom ingestion.
Scheduled data flows. Set recurring jobs to refresh embeddings and indexes so the AI sees up-to-date data without manual runs.
Deployment options. Use SOC 2 compliant cloud, self-hosted, or on-prem so security and compliance teams choose the right boundary.
Vector and embedding integrations. Plugs into common embedding models and vector databases so teams keep their existing AI stack.

Who Context Data is for

SMB engineering teams shipping a first internal AI assistant who do not want to build an ingestion pipeline themselves.
Enterprise data leads who need on-prem RAG to meet regulatory or contractual data-handling requirements.
AI product teams iterating on retrieval quality who want infra they can swap embedding models inside.
Consultancies delivering RAG projects who want a shared framework instead of bespoke pipelines per client.

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