All use cases

Engage / Lifecycle

Rich-UI self-serve data exploration in thread

Your customer asks "how's my sleep been lately?" — instead of a wall of text, the agent draws them a chart they can poke at and a setting they can change, right there in the thread.

Next best thingBehavioral triggerChatIn-appSubscriptionHealth & TelehealthFintechWellness (B2C apps)

What it does

Products sit on top of the data their users care most about — sleep stages, spending, training load, account activity — and then answer questions about it with either a paragraph of prose or a link to a dashboard the user has to go hunt through. Both kill the moment: the user asked a question because they were curious right now, and the friction of leaving the conversation is where curiosity and engagement die. The agent answers with real, interactive interface rendered inline — a trend chart they can scrub, a breakdown they can expand, a setting they can toggle — so the user explores their own data and acts on it without ever leaving the thread. The conversation becomes the place to understand yourself and the product, which is exactly the surface that keeps people coming back.

How it works

  1. 1
    Trigger. the user asks a question about their own data in the thread — "how's my sleep been this week?", "where did my spend go?", "am I training too hard?"
  2. 2
    Decision. the agent reads the request and the user's memory, pulls the relevant data through a read-only query, and decides which interface element actually answers the question — a trend line, a category breakdown, a comparison, or a control the user can adjust. The rendered output passes a quality check before it shows.
  3. 3
    Action. it renders the live UI inline — an interactive chart, an expandable breakdown, or a setting toggle — with a one-line plain-language read of what it shows, and the user explores it in place: scrub the timeline, expand a category, flip a control.
  4. 4
    Follow-through. any change the user makes through a rendered control is confirmed before it is applied and written back; the exchange writes to memory so the next question builds on this one; a question that goes beyond the user's own data — or anything requiring advice or a decision the agent shouldn't make — hands off to a human.
Example
CustomerHow's my sleep actually been lately? It feels worse.
AgentHere's your last two weeks — *[renders an interactive sleep-trend chart]*. Total time is steady, but your deep-sleep share has dropped on the nights you logged a late workout. Scrub the chart to see any night, or tap a bar to break it into stages. Want me to turn on a wind-down reminder on late-training days?
CustomerYeah, let's try that.
AgentDone — I've set a wind-down nudge for nights you train after 8pm. *[renders the new setting as a toggle you can switch off anytime]* I'll check back in a couple of weeks to see if the deep-sleep dip eases.

Configuration

How the agent is wired for this use case.

Triggeran in-thread user question about their own data (a data_query intent classified from the conversation), with the user reference and the data domain.
Tools & actions
  • Data / metrics store · run a read-only query for the user's own series (sleep, spend, training, account activity) over the asked window
  • Generative-UI renderer · assemble the right interactive element inline (trend chart, category breakdown, comparison, or a setting control)
  • Settings / preferences service · apply a user-confirmed change made through a rendered control and write it back
  • Customer memory · read context to scope the query and write back what was explored and adjusted
  • Messaging channel · deliver the rendered UI and the plain-language read in the conversation
Autonomyquerying the user's own data and rendering the exploration UI run unattended, with the rendered output quality-gated before display. Any change made through a control requires explicit user confirmation before it is applied; nothing money-moving or clinical is executed by the agent — for the health domain it surfaces the user's own data without diagnosing, and for the financial domain it explores and explains without giving advice or moving money.
Channelschat · in-app
Escalationa request beyond the user's own data, a clinical or financial-advice question, or a change the agent isn't permitted to make hands off to a human with the conversation context.

What you need

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

Signals

in-thread data questions from the user and the data domain they refer to

Data

the user's own metrics/time-series, the schema for what can be queried and rendered, the set of settings the user may change in-thread, consent and memory

Guardrails

read-only access to the user's own data; rendered output quality-gated before it shows; every in-thread setting change confirmed by the user before it applies; in the health domain the agent surfaces data without diagnosis or clinical interpretation, in the financial domain it explains without advice or money movement; questions beyond the user's data escalate to a human

Metrics it moves

  • dau-mauup: data questions become interactive exploration sessions in-thread instead of one-line answers or an abandoned dashboard trip
  • churndown: the conversation becomes the place users return to understand their own data and adjust the product
  • ltvup: deeper, self-serve engagement on the user's own data sustains the subscription relationship over time

Related use cases

Engage / Lifecycle

Test-result interpretation agent

a clinician-grade reading of a single result, distinct from open-ended data exploration

Engage / Lifecycle

Proactive bubbles and personalized push

agent-initiated nudges, where this is user-initiated in-thread exploration

Engage / Lifecycle

Wearable-driven rest and recovery nudge

acting on the same wellness data via a proactive nudge rather than in-thread UI

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