Hallucination-Proof AI for Complex CX
Get a sneak peek into the new CX leader’s guide to agentic workflows & deterministic AI guardrails.
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Why complex CX
needs guardrails
As support requests get more complicated, the risk of errors and hallucinations increases.
Deterministic guardrails behind hallucination-proof AI
A multi-layer control framework filters outputs through a series of configurable guardrails, ensuring accuracy and compliance.
Understand what the
user really wants
Before AI generates anything, Zingtree identifies what the user is asking and refines the query for clarity. This ensures the model fully understands the request, filters out irrelevant or unsafe inputs, and maps the message to a predefined intent that sets boundaries for the next stage.
Mechanisms:
- Detect and filter irrelevant, incomplete, or unsafe queries before they reach the model.
- Refine and structure valid inputs so the LLM can interpret them correctly.
- Map each query to a predefined intent, linking it to the right rules, workflows, and data sources.
The result of control
In our CX Leader’s guide, see how to ensure AI answers and actions are hallucination-proof and ready for the real world.
