All use cases

Engage / Lifecycle

Test-result interpretation agent

Your customer just got their hormone results and is staring at numbers they don't understand. The agent reads them in the light of everything that came before.

Close the loopGet aheadSystem triggerIn-appChatHealth & TelehealthWellness (B2C apps)GLP-1 & Peptides

What it does

An at-home test result is the moment of highest attention in the whole product: the customer is curious, often anxious, and ready to engage. Most apps answer that moment with a chart and a reference range, and the engagement dies right there. The agent interprets each new result against the customer's full history: what the numbers mean in plain language, how the trajectory has moved since the last test, and what to pay attention to next. Every result becomes a conversation instead of a dead end, and the interpretation layer becomes the foundation for a product's own AI coach.

How it works

  1. 1
    Trigger. a test_result_ready event arrives from the result-processing pipeline when a new test is scored.
  2. 2
    Decision. the agent loads the customer's complete result history and memory (goals, cycle context, prior conversations), compares the new value against the customer's own baseline rather than only the population range, and checks consent and clinical boundaries. Every interpretation passes the quality judge before the customer sees it.
  3. 3
    Action. an in-app message opens the result with a plain-language read: what this number means, how it sits against their own history, what the trend suggests. The customer can ask follow-up questions in the same chat thread.
  4. 4
    Follow-through. values that cross red-flag thresholds skip self-serve interpretation and route to clinical escalation. The conversation writes back to memory so the next result is read with this one in context. Rollout is staged: offline quality comparison against expert interpretations first, then 5% of result events, then 15%, with retention measured against a holdout group before going wider.
Example
AgentYour latest result is in. Compared with your previous three tests, this hormone is peaking earlier in your cycle than your own usual pattern. On its own that is within the normal range; here is what it tends to mean.
CustomerIs that something I should worry about?
AgentNot from a single reading. I will watch how your next test compares with this one, and if the shift holds, I will suggest bringing it to a clinician with your full history attached.

Configuration

How the agent is wired for this use case.

Triggera test_result_ready event from the result-processing pipeline when a new test is scored
Tools & actions
  • Result-processing pipeline · read the scored result and its reference ranges
  • Customer memory store · load complete result history, goals, cycle context, and prior Q&A
  • Messaging channel · open the result in-app and hold the follow-up conversation in the same chat thread
  • Clinical escalation path · route red-flag values to the clinical team
  • Memory store · write the conversation back so the next result is read in context
Autonomyinterpretation runs unattended behind the quality judge; every read is judge-gated and framed as education, never diagnosis. Any value crossing a red-flag threshold is governed by the clinical-escalation policy class and is handed to a human rather than interpreted. Rollout is staged behind an offline quality benchmark before scale-up.
Channelsin-app · chat
Escalationvalues that cross red-flag thresholds skip self-serve interpretation and route to clinical escalation.

What you need

The inputs this use case runs on. Your channels stay yours; the agent supplies the judgment.

Signals

test_result_ready or an equivalent result-processed webhook from the lab or device pipeline

Data

complete per-customer result history, reference ranges, customer memory (goals, cycle data, prior Q&A), consent state

Guardrails

judge gating on every interpretation; educational framing, never diagnosis; red-flag values escalate to humans instead of being interpreted; staged rollout behind an offline quality benchmark; holdout measurement before scale-up

Metrics it moves

  • churndown: a result the customer actually understands is a reason to stay subscribed for the next one
  • ltvup: retention measured against a control group, modeled into revenue per the staged-rollout design
  • dau-mauup: every result event converts into an in-app conversation instead of a glance at a chart

Related use cases

Upsell & Cross-sell

Lab-result-driven product personalization

the commerce sibling of the same result moment

Prevent / Care

Clinical red-flag detection and safe escalation

where interpretation must stop and humans take over

Engage / Lifecycle

Proactive health-coach companion

the coach this interpretation layer grows into

See it on your own customer journey

Bring one drop-off, one churn cliff, or one silent segment. We will show you what a proactive agent with memory and judgment does with it.

Book a demo