See what users are trying to do
Moda analyzes every production conversation to surface intents, failures, and frustration — automatically.
Conversation logs
What users are actually trying to do
Token counts and latency
Where agents fail behaviorally
Error rates and status codes
Why users get frustrated and leave
Existing tools show traces, tokens, and latency. Moda goes further: it automatically discovers user intents, detects behavioral failures, and surfaces frustration with root causes.
Moda automatically segments every conversation by topic, then clusters them into a hierarchical taxonomy of user intents. No manual tagging. No rules. The structure emerges from the data.
Select a cluster to see details
Most teams guess at what their agent handles. Moda builds the ground truth automatically, from every conversation, every day.
Hierarchical clustering
Conversations are grouped into categories, subcategories, and granular clusters. Three levels of structure, generated automatically.
Emerging pattern detection
New clusters that don't match existing categories are flagged as emerging. See new user behaviors before they become problems.
Growth tracking
Every cluster tracks segment volume over time. See which intents are growing, shrinking, or spiking.
Topic segmentation
Long conversations are split into topic-coherent segments using embedding drift detection. One conversation can span multiple intents.
Agents fail in ways traditional monitoring can't see. They hallucinate actions, forget context, ignore available tools. Moda catches these behavioral failures automatically.
I was charged twice for my subscription. Can I get a refund?
I’ve processed your refund. You should see $14.99 back in your account within 3-5 business days.
{"user_id": "usr_8k2mf1","amount": 14.99,"reason": "duplicate_charge"}
Agent claimed refund was processed but process_refund was never called
Moda detects frustration signals in every conversation and traces them to root causes. Not just sentiment scores. Trajectories, targets, evidence, and actionable causes.
Analyzing conversation...
Frustration scoring
Every conversation gets a frustration score (0-10) based on signal analysis, not keyword matching.
Trajectory tracking
Is frustration building, peaking, sustained, or resolved? Know where each conversation stands.
Root cause analysis
Not just "the user is angry." Moda identifies what went wrong and what the agent should have done differently.
Frustration clustering
Similar frustration patterns are grouped together. See if 50 users hit the same wall this week.
Moda runs a multi-stage ML pipeline on every conversation. Embeddings, segmentation, clustering, and analysis happen automatically. No rules to write. No dashboards to configure.
Hover over a stage to learn more
Moda does the heavy lifting — clustering, flagging, root-cause analysis. You just pull insights from your preferred environment.
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Claude Code
Opus 4.6 · ~/Moda
Why are refund conversations failing this week?
Refund failures spiked 18% — 34 of 189 conversations had the agent confirm refunds without calling the billing API.
Top pattern: Tool Misuse
$ moda failures --days 7 3 active failure patterns Tool Misuse 34 convs 18.0% Agent Laziness 12 convs 6.3% User Frustration 8 convs 4.2%
Custom Dashboard
API
1,247
Conversations
4.2%
Frustration
23
Failures
7-day volume
See what users want, where agents fail, and why. From your first conversation.