AI for contact centers and customer service teams

Design the complete service journey

Automating the start of a call is only part of the job. You also need to define what happens if the inquiry requires an exception, if the information falls short or if the caller needs to speak with an operator.

The operation must set responsibilities: what the AI handles, what the human team resolves and what context each one needs. MEHI lets you work on that journey and review the calls that show where it breaks down.

Knowledge shared between AI and people

An answer can be wrong even if the agent converses well, if it relies on an outdated procedure. That is why it pays to govern the content that feeds the service: owners, reviews, versions and publishing rules.

MEHI is complemented by KORENUS to connect institutional knowledge and human case handling. The specific capabilities and the context available at each transfer are validated against the project's systems and telephony.

What to look at when evaluating results

The outcome has to be defined for each type of inquiry. A correct handoff can be the expected result; closing a call without resolving it should not automatically count as a success. The review should combine indicators with evidence from real cases.

• Accuracy of the information provided.
• Adherence to the expected path for each need.
• Handoff reasons and continuity of service.
• Cases that require correcting content or instructions.
• Impact of changes, with a comparable before-and-after measurement.

An evaluation tied to your operation

To evaluate MEHI, bring a concrete process, its frequent questions, its exceptions and the current path to an operator. On that basis we can agree on a verifiable scope and review the integrations required.

We do not publish universal savings or resolution percentages: results depend on the process, the available knowledge and the implementation.