The system that watches your CX data so your team can act on what matters
Unseam connects to your customer data, groups conversations automatically, tracks patterns over time, and alerts you when something starts moving in the wrong direction. No configuration required to start seeing value.
How Unseam works
Ingest
Connect your CX data.
Support transcripts, tickets, CSAT and NPS responses, and interaction metadata. We meet your data where it lives.
Cluster
AI groups similar conversations automatically.
No tagging dependencies. No rules to configure. Conversations are converted to embeddings and grouped by semantic similarity, so issues that look different on the surface get treated as one pattern.
Detect
Continuous monitoring against your baseline.
Patterns are tracked against rolling historical windows. When something deviates, even before it’s obvious, Unseam flags it. The earlier you know, the less it costs.
Explore (Optional)
Go deeper when you want to.
Alerts and patterns run automatically. Most teams get the value without touching this layer. But if you want to investigate further, customize what you track, or fine-tune your thresholds, you can. The system keeps improving the more you engage with it.
A closer look at what’s under the hood
See your issues as clusters, not one-offs
Unseam converts every conversation into a vector embedding and groups semantically similar ones together, without requiring consistent agent tagging or predefined categories. What looks like scattered individual tickets often turns out to be one coordinated pattern affecting hundreds of customers.
The UMAP visualization shows your entire conversation landscape at a glance. Zoom in on any cluster to see volume, trend direction, and a sample of the underlying conversations.
Flags the weird stuff before your team notices it
Every cluster is tracked against a rolling baseline. When volume, sentiment, or composition deviates from normal, even by a subtle amount, Unseam flags it. The z-score normalization means you’re not reacting to absolute volume. You’re reacting to change relative to your own patterns.
A heat map view shows you which clusters are moving and how fast. Time series plots let you trace the arc of an issue from first signal to resolution.
Ask questions directly against your data
When a cluster or alert needs a closer look, you can query the underlying conversations in natural language. Filter by time window, cluster, or custom criteria. Read the actual tickets. Pull patterns out of the text.
This is the layer for teams that want to go beyond the alert: root cause analysis, drafting response playbooks, understanding what customers are actually saying. Most alerts won’t need it. The ones that do will get it.
Works with your CX data wherever it lives
If your data is accessible programmatically, Unseam can work with it. We’re building toward native integrations with the major support platforms, and we’ll prioritize based on what our early customers are actually using.