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Powered by Gemini

Built with Google Cloud and Gemini

Terra runs on Google Cloud and uses Gemini models for drafting, record chat and summarisation. This page explains where Gemini sits in a visit, why we chose it, and how patient data is handled along the way.

Google Cloud Gemini
Structured response · schema: follow-up-visit@v3
{
  "section": "assessment",
  "sentences": [
    {
      "text": "Left knee osteoarthritis (M17.12), improving on NSAID therapy.",
      "citations": ["S2", "S5"]
    }
  ],
  "unsupported": []
}

Where it is used

Three jobs, each with a citation trail.

  • Drafting

    Visit notes drafted section by section in the clinician's template, with citations to the transcript and chart.

    AI Scribe

  • Record chat

    Questions about a patient's reviewed record, answered with a citation on every claim.

    Record Chat

  • Summarisation

    What changed since the last visit, condensed from signed notes and approved facts for the Context tab.

    Context Engine

Why Gemini

Structure, context and where the data lives.

01

Structured outputs

Gemini returns the note as typed JSON that matches the template's sections and fields, so every sentence keeps its citation and nothing is lost in parsing.

A clinical note is not free text to Terra. Each template defines sections, typed fields and instructions, and each drafted sentence has to carry the IDs of the sources it relies on.

Gemini's structured output mode lets Terra hand the model a response schema and receive JSON that conforms to it. Sections arrive in order, fields arrive typed, and citations arrive as arrays of source IDs that Terra verifies against the transcript and chart before showing the draft.

02

Long context for whole charts

Prior signed notes, approved facts and today's transcript fit in one request, so the draft reads the chart itself rather than a summary of it.

Good notes depend on what came before: the medication started last month, the allergy recorded two years ago, the plan from the last visit.

Gemini's long context window lets Terra include the full set of sources a clinician enables for a visit, each labelled and pinned to its version, without lossy pre-summarisation. Record Chat uses the same property to answer questions across a patient's reviewed history with a citation on every claim.

03

Data residency on Google Cloud

Model requests run on Google Cloud next to the rest of Terra, in the regions we choose, covered by a Business Associate Agreement.

Terra's infrastructure runs on Google Cloud. Calling Gemini through Google Cloud keeps model traffic inside the same cloud, in the regions Terra selects, rather than sending protected health information to another provider's network.

Google Cloud offers a Business Associate Agreement for covered services, and its enterprise AI terms state that customer data is not used to train Google's models. Terra adds its own controls on top: minimum necessary inputs, no names or MRNs in prompts, and audit records for every request.

Data flow

From the room to a signed note.

Every step runs on Terra's infrastructure on Google Cloud, and the clinician stays in control of the record.

  1. Step 1

    Capture

    Audio is captured in the browser and transcribed. Speakers are confirmed by the clinician.

  2. Step 2

    Assemble

    Terra's API on Google Cloud gathers the sources the clinician enabled, labels and pins each one, and removes names and MRNs.

  3. Step 3

    Generate

    Gemini returns the note as structured JSON that matches the template schema, with source IDs on every sentence.

  4. Step 4

    Verify

    Terra checks every citation against the pinned sources and flags anything unsupported before the draft is shown.

  5. Step 5

    Review

    The clinician edits and signs. The signed text is frozen and hashed; Gemini never writes to the record directly.

Security controls, Business Associate Agreements and the full subprocessor list are in the Compliance center.

FAQ

Gemini and your data

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