When users talk to a chatbot, intent accuracy determines everything that follows. If the agent misunderstands what the user wants, the rest of the conversation collapses. The response can sound fluent and still be wrong.
Cekura gives teams a way to systematically test, score, and monitor intent accuracy across real conversational conditions, before issues reach users and after every change in production.
What Intent Accuracy Really Means in Practice
Intent accuracy is not a single classification step. In real conversations, it includes:
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Recognizing the correct intent even when users phrase requests differently
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Maintaining the same intent understanding across multi-turn exchanges
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Avoiding intent drift when the user adds constraints or corrections
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Choosing the correct workflow, tool call, or next action based on intent
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Staying aligned with the agent’s instructions and business rules
Cekura evaluates intent accuracy at the conversation level, not just at one turn.
Scenario-Based Intent Validation
Cekura generates and runs structured conversational scenarios that reflect how users actually speak to chatbots.
These scenarios cover:
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Variations in phrasing, tone, and structure for the same intent
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Ambiguous or overlapping intents that require disambiguation
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Multi-turn intent clarification flows
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Edge cases where intent changes mid-conversation
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Long conversations where intent must remain consistent over time
Each scenario tests whether the chatbot selects and follows the correct intent path from start to finish.
Intent Accuracy Metrics That Reflect Real Behavior
Cekura evaluates intent accuracy using multiple complementary signals rather than a single pass or fail label.
These include:
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Instruction adherence tied to the agent’s defined intent logic
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Relevancy and response consistency across turns
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Detection of hallucinated intent shifts
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Verification that the correct downstream tools or APIs were triggered
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Confirmation that required intent-specific steps were completed
Metrics can be predefined, customized, or fully programmable, allowing teams to match evaluation logic to their actual workflows.
Confusion Detection Across Similar Intents
Many chatbot failures happen between intents that look similar on the surface.
Cekura helps teams identify:
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Intents that are frequently confused with each other
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Scenarios where intent selection depends on subtle wording differences
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Cases where the agent partially follows one intent while answering another
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Situations where intent accuracy degrades under stress, latency, or interruptions
This makes it easier to refine prompts, routing logic, and fallback behavior with evidence rather than guesswork.
Multi-Turn Intent Consistency Checks
Intent accuracy often fails later in the conversation, not at the start.
Cekura tracks whether the chatbot:
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Remembers the original intent after several turns
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Correctly updates intent when the user changes their request
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Avoids reverting to an earlier intent incorrectly
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Maintains intent alignment while handling interruptions or clarifications
Failures are flagged with timestamps, transcripts, and metric evidence to make debugging fast and precise.
Persona-Driven Intent Testing
Users express intent differently depending on who they are and how they speak.
Cekura simulates conversations using varied personas, including:
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Different communication styles and verbosity levels
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Interruptive or impatient users
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Users who provide incomplete or messy information
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Non-standard phrasing, slang, or indirect requests
This ensures intent accuracy holds across realistic user behavior, not just ideal prompts.
Regression Testing for Intent Accuracy
Every prompt change, model update, or infrastructure change can break intent handling.
Cekura allows teams to:
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Lock intent accuracy baselines
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Automatically re-run the same intent scenarios after changes
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Compare intent performance across versions
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Detect regressions before deployment
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Track long-term intent stability over time
This turns intent accuracy into a measurable, enforceable quality bar.
Production Monitoring for Intent Drift
Intent accuracy issues do not stop after launch.
Cekura monitors production conversations to identify:
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New intent failure patterns
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Drift introduced by model updates or traffic changes
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Unexpected intent misclassification under real load
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Scenarios where users abandon conversations due to intent errors
Teams can set alerts when intent accuracy drops beyond acceptable thresholds, allowing fast response without manual review.
Built for Chatbots That Do Real Work
Cekura is designed for chatbots that handle real workflows, not demos.
That includes agents that:
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Route users through multi-step processes
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Trigger backend systems and APIs
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Enforce business rules and compliance constraints
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Handle sensitive or high-stakes interactions
Intent accuracy is evaluated in the context of what the chatbot is supposed to accomplish, not in isolation.
Turn Intent Accuracy Into a Measurable System
With Cekura, intent accuracy becomes something teams can test, track, and improve continuously.
Instead of relying on spot checks or intuition, teams get structured evidence of how well their chatbot understands users across scenarios, versions, and real-world conditions.
Intent accuracy stops being an assumption and becomes a measurable property of the system.