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DATA ENGINE

The data layer for Trusted AI.

Bring anything: documents, media, whole archives. Data Engine makes it understood, citable, and ready to ground any model, privately and at scale.

At capacity: request accessSee how grounding works

HOW IT WORKS

  1. Ingest

    40+ formats: documents, spreadsheets, audio, video, whole channels and archives. Media is transcribed with timecodes, rights attach to every file on arrival, and you watch ingestion happen live.

  2. Understand

    Every passage gets ~80 fields of understanding: the seven dimensions of human flourishing, faith-specific analysis down to the verse, topics connected into a graph. Your content becomes a map, not a pile of vectors.

  3. Retrieve

    Ask in plain language. Semantic search with reranking and filters across every field, and every answer comes back as a citation you can stand behind: series, episode, moment.

From your data to a grounded answer in one call.

Point a request at your data. The answer comes back grounded, built from your sources and citing them, down to the moment. Nothing to assemble yourself.

Retrieval, grounding, and attribution in one call.

Your data stays yours.

  • Every organization's data is isolated: native multi-tenancy, enforced on every request.

  • We don't train on your data. Ever. You pay for the platform; what you put on it stays yours.

  • Rights travel with the content: what an app may show, quote, or preview is enforced at the data layer, not the honor system.

  • Retention, export, and deletion built in.

Licensing & digital rights management
Distributing content under rights agreements? Usage rules per relationship, preview limits, training opt-outs, versioned licenses with a full audit trail. Enterprise only. Every rights setup is different, so let's talk about yours.
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Your data and content deserve infrastructure that grows with them.

A vector database stores what you give it. Data Engine understands it, protects its rights, and cites it.

  • Content

    DIY search / RAG / context stack
    Whatever you scraped
    Data Engine
    Your content and data, at scale

  • Understanding

    DIY search / RAG / context stack
    Generic embeddings
    Data Engine
    ~80 fields per passage, flourishing and biblical ontologies, a topic graph

  • Rights

    DIY search / RAG / context stack
    None
    Data Engine
    Enforced at the data layer

  • Citations

    DIY search / RAG / context stack
    If you build them
    Data Engine
    Structured by default, down to the timecode

  • Grounded answers

    DIY search / RAG / context stack
    You wire it
    Data Engine
    One call, with Inference

You could assemble this from four tools. You'd still be missing the content, the ontology, and the rights.

PLANS

Data Engine is currently at capacity.

More capacity is coming in the next few weeks to make sure your data, context, and memory are ready to power whatever you need.

Request access

FAQ

THE REST OF THE PLATFORM

Build with Trusted AI.

At capacity: request accessRead the docs
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