Last updated: 2026-09-21 · 3 min readBusiness〜

Signal Detection

The AI agent analyzes customer behavior data in real time and automatically detects signals that affect how a client project moves forward. Detected signals appear in the Decision Timeline and help inform the right action.

Signals in the Decision Timeline

Types of signals

The agent detects the following eight types of signals.

Buying intent signals

Signal	Description	Typical severity
Decision-maker access	A stakeholder with decision authority accesses the Room	high
Pricing interest	Viewed pages related to pricing or quotes	medium–high
Content deep-read	Viewed a specific page for an extended time	medium
Bulk review	Viewed multiple pages together in a short period	medium

Risk signals

Signal	Description	Typical severity
Engagement decline	Access frequency and viewing time decrease	high–critical
Competitor comparison	Viewed a page comparing competing products	high

Stakeholder signals

Signal	Description	Typical severity
Champion activity	Active behavior by an internal champion	medium
New stakeholder	A new stakeholder joins or accesses the Room	low–medium

Severity levels

Each signal is assigned one of four severity levels.

Level	Color	Meaning
critical	Red	Immediate action required. May directly stall or advance the client project
high	Orange	Early action recommended. Risk grows if left unaddressed
medium	Yellow	A change worth noting. Understand the situation and consider the next action
low	Blue	For reference. Often does not require direct action

How to review signals

Reviewing in the Decision Timeline

Each Room's Decision Timeline displays AI signals and customer reactions in chronological order.

Open the target Room from the sidebar
Click "Decision Timeline" in the left menu
Choose "AI signal" in the type filter and review the timeline

Each signal shows the following information.

Signal type and icon
Severity badge (color-coded)
Description (a summary of what happened)
Detection date and time

To understand the background of a signal, review the timeline item and then ask the AI sidebar, "Organize the background of this signal and the next action."

Reviewing on the dashboard

Insights lets you review which Rooms need attention across the workspace. For details, see Client-project portfolio intelligence.

The relationship between signals and actions

When the agent detects a signal, it automatically proposes actions appropriate to the situation.

Example signal	Example proposed action
Decision-maker access	Send a follow-up message
Engagement decline	Create a task to follow up with the CTO by phone
Competitor comparison	Send material emphasizing your differentiation points
New stakeholder	Send an introduction message

The agent learns from your patterns of approving and rejecting actions for signals, and its proposals become more accurate over time. By giving feedback proactively, you'll get proposals that better match your team's sales style.

Excluding bot access

The agent automatically filters out access from crawlers and bots, and treats only human behavior as a target for signal detection. This prevents false positives and provides reliable signals.

Signal detection accuracy improves in proportion to the amount of data accumulated in the Room. In a new Room, the number of detected signals may be low until enough data has accumulated.