How Cekura Validates Chatbot Intent and Entity Recognition at Scale

Chatbots break down when they misunderstand what users want or extract the wrong details. A missed intent. A partial entity. A small error that derails the entire workflow.

Cekura gives teams a reliable way to verify intent classification and entity recognition across real conversations, before and after deployment.

Built for modern chat agents, Cekura helps you prove that your bot understands users correctly, not just that it responds fluently.

Validate Intent Understanding Across Real Scenarios

Cekura tests whether your chatbot consistently identifies the right intent, even when users phrase requests differently.

You can simulate hundreds of variations for the same goal, including paraphrases, typos, slang, short replies, and multi-intent inputs. Each run checks whether the agent routes the conversation to the correct workflow and follows the expected path.

This makes it easy to catch:

Confusion between similar intents
Incorrect fallback or escalation triggers
Intent drift after prompt or model changes
Edge cases where intent is partially understood but misapplied

Measure Entity Extraction With Precision

Correct intent is not enough if the details are wrong.

Cekura verifies that entities like names, dates, IDs, locations, order numbers, and policy values are extracted, retained, and reused correctly across turns.

You can validate:

Exact vs partial entity matches
Missing or hallucinated entities
Entity overwrite or loss in long conversations
Incorrect reuse of prior user inputs

Entity checks run at the conversation level, not just on single messages, so you can see where and when information breaks down.

Multi Turn Context and Memory Validation

Real users do not repeat themselves perfectly. Cekura tests whether your agent remembers earlier inputs and applies them correctly later in the conversation.

This includes verifying that:

Entities introduced early are recalled accurately
Follow-up questions reference the correct prior context
Long conversations maintain consistency without drift

Failures are timestamped so teams can pinpoint exactly where context was lost.

Intent and Entity Accuracy Scoring You Can Act On

Cekura turns intent and entity validation into structured metrics that are easy to track over time.

Teams can:

Score intent accuracy across test suites
Break down results by intent type or scenario
Flag false positives and false negatives
Compare performance across models, prompts, or versions

These metrics plug directly into regression testing, A/B testing, and CI workflows so accuracy does not silently degrade.

Explainability for Faster Debugging

When an intent or entity check fails, Cekura shows you why.

Each issue is tied back to:

The specific turn where the failure occurred
The expected versus actual outcome
The surrounding context that caused confusion

This shortens the path from detection to fix and removes guesswork from debugging.

Production Monitoring for Intent and Entity Drift

Cekura does not stop at pre deployment testing.

In production, you can monitor real conversations to detect:

New intents your bot is failing to recognize
Entity extraction errors caused by real user behavior
Accuracy drops after traffic, model, or infrastructure changes

Alerts and dashboards surface issues early, before they impact user experience at scale.

Built for Teams Shipping Real Agents

Cekura works with chat and voice agents across customer support, healthcare, finance, and enterprise workflows. It is designed for teams that need confidence, auditability, and repeatability, not one off demos.

If intent accuracy and entity correctness matter to your product, Cekura gives you the tools to prove it.