LLM Insights

LLM Insights turn the physiological metrics the SDK computes on-device (pulse, heart-rate variability, breathing) into natural-language analysis produced by a large language model. The SDK buffers a rolling window of metrics, sends that window — optionally with a prompt you supply — to the Presage 3.0 Analytics Gateway, and delivers the model's reply to your application as an Insight.

This page is the primary reference for the feature: the concepts, the common API shape, configuration, and privacy. For exact signatures and runnable examples, follow the link to your platform's guide.

How it works

LLM Insights follow a request/response model. There are two ways a request is dispatched, and both deliver their response through the same sink:

• Auto-fired vitals — once a session is running, the SDK automatically dispatches a vitals snapshot of the accumulated metrics buffer every 15 seconds of processing (no prompt).
• On-demand — you request an insight with a prompt. If the metrics buffer already holds vitals at dispatch time, the request is combined (your prompt plus the latest metrics); if the buffer is still empty, it is prompt-only.

The physiological metrics are what make the analysis specific to the user: the model receives the buffered metric series, not just your prompt text, so it can ground its response in the measured pulse, HRV, and breathing. Responses are asynchronous — there is no blocking call that returns an analysis inline.

Common API interface

Every platform exposes the same two operations, in a platform-idiomatic form:

1. Request an insight — submit a prompt on a running session. It returns a request ID that identifies the reply. The prompt is combined with the latest buffered metrics when they exist, otherwise sent prompt-only.
2. Receive responses — register a single sink that receives all insight responses (both auto-fired vitals and on-demand replies). Each response is an Insight carrying either analysis (success) or error (failure), plus a request_id. Correlate an on-demand reply with the request that produced it by matching its request_id (auto-fired vitals won't match a request you made). Every insight is currently delivered with type INSIGHT_TYPE_VITALS (SPEECH/COMBINED are reserved), so use request_id, not type, to tell them apart. The Insight type is documented in Data Types → Insight.

How each platform surfaces these:

See your platform's guide for exact signatures, setup, and examples: C++ · Android · Swift · Node.js.

For guidance on writing prompts the model can act on, see Writing Effective Prompts.

When a request fails to send or a response carries an error, see LLM Insights: Error Handling & Troubleshooting for the two error surfaces, common failure modes, retry guidance, and UI patterns.

Required metrics configuration

Insights are only meaningful when the metrics they summarize are being computed:

• Breathing (defaults) and cardio must both be active. The SDK buffers pulse rate, HRV, and breathing rate for the insight payload, so those metric groups must be enabled. Each platform guide shows the exact configuration call.
• ARTERIAL_PRESSURE_TRACE is available for visualization. It drives the on-screen pulse waveform and is part of the cardio metrics.
• Allow warm-up time before insights are meaningful. The metrics buffer starts empty and fills as valid measurement accumulates. The first auto-fired vitals insight is dispatched about 15 seconds after the session starts, and an on-demand request made before the buffer has filled is sent prompt-only (no metrics). Wait for at least that long before expecting analysis grounded in the user's physiology.

Privacy & data notice

When an insight is dispatched, the SDK sends the following to the Presage 3.0 Analytics Gateway, which forwards it to the LLM:

• The buffered metric values — an allowlisted subset of the computed metrics (pulse rate, heart-rate variability, and breathing rate) as a columnar series.
• The prompt text you supply, when present.
• Request metadata (a session ID, request origin, and request ID).

Raw video and facial imagery are never transmitted — only the derived numeric metric values and your prompt leave the device for this feature.

Important: LLM Insights are generated by a large language model and can be inaccurate, incomplete, or misleading — language models sometimes produce confident but false statements ("hallucinations"). Treat every insight as general wellness information, not medical advice, and confirm anything important before acting on it. The SDK metrics themselves are offered for general wellness and informational purposes only; they have not been cleared by the FDA and may not be used for medical diagnosis or treatment.

Getting Help

• Email: support@presagetech.com
• Submit a GitHub issue
• Docs and FAQ
• Developer Admin Portal