Q & A

Each organization will have its own business metrics aligned to its strategic goals.
Measuring the impact of the AI applications against those metrics will either validate
the direction or signals when a pivot is needed.

In particular, for agents, we need to cover multiple aspects of the implementation:
Data: The quality of the data will be a key factor. Many customers start with a pilot using perfectly clean data, and get a false sense of performance. Testing with realistic data sets and identifying data gaps early will expose weak spots before they become problems.

Guardrails: Multiple types of guardrails can be implemented to avoid undesired output. At a minimum, include security guardrails, response filters, and topic restrictions to keep the model within acceptable boundaries.

Go live as early as possible and iterate fast: Even if tests are successful, it’s not rare for businesses to find that when the agent is deployed into the real world, the outcomes are surprisingly different. Be prepared; assume that your agents will require quick intervention and ‘on-the-fly’ prompt adjustments to improve their reliability.

Continuous monitoring and refining: Set up a robust testing and feedback framework. Break down test cases into specific scenarios. Clearly define benchmarks.
Keeping a human-in-the-loop means you remain in control, at least at the early stages, reviewing edge cases, correcting errors, and steadily improving the system with each cycle.

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This approach directly informs how we design and operationalize AI at SearchUnify. Guardrails such as security controls, response filters, and intent-based restrictions are embedded across solutions like Agent Helper and Cognitive Search to promote safe, reliable, and context-aware outputs.

We also maintain a strong focus on human-in-the-loop oversight. While AI handles repetitive tasks and surface-level decisions, human judgment ensures alignment with strategic goals and enterprise context. This balanced collaboration between machine and human intelligence supports continuous learning, drives operational efficiency, and safeguards both quality and trust.

The complexity of using AI in businesses has multiple dimensions. On one hand, we have this technology that is very powerful, but it’s non-deterministic. We cannot fully predict how it will respond. This is a shift of paradigm from traditional software and automation. On the other hand, we have additional new challenges, such as bias, regulations and the societal impact of AI.

I believe that educating the workforce is absolutely essential, starting from an AI-enabled leadership. AI literacy involves not only knowing how to leverage AI for better results but also understanding how AI works, what the risks are, and how to deal with hallucinations, data privacy, unpredictability and ethical concerns.

An important lesson is to start with simple use cases with low risk and keep a “human-in-the-loop” approach. Starting with complex use cases before the organization has reached AI maturity could lead to early frustration and a discouraged team.

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At SearchUnify, we believe responsible AI starts with built-in safeguards that address core risks from day one. Our solutions incorporate features that mitigate key challenges like hallucinations, data privacy, and offensive content. SearchUnifyFRAG™ grounds LLM responses in verified enterprise knowledge to reduce hallucinations and deliver accurate, context-aware answers. PII Masking protects sensitive and personally identifiable information in real-time.

Meanwhile, our Content Flagging mechanism helps detect and flag biased, offensive, or harmful outputs for human review, ensuring accountability and ethical alignment. Coupled with a human-in-the-loop approach, these safeguards support safe, scalable AI adoption that builds trust and accelerates ROI.

Specifically for AI implementations, we have a special type of metric called “Evals”. These metrics evaluate the quality of LLM-based applications and are a critical component of any real-world AI rollout.

There are a variety of options when it comes to Evals; an example is evaluating the output of a support chatbot based on relevance, tone, accuracy, clarity, and grammar—test for hallucinations, bad answers, security gaps, and bias. Expose the weak spots before they cause damage.

Evals should be both broad and deep. Broad, to cover all the key dimensions of performance. Deep, to stress-test the system under realistic and edge-case scenarios. The more specific and granular the Evals, the easier it becomes to troubleshoot and debug failure modes, measure progress, and deploy targeted fixes.

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Alejandra’s perspective closely aligns with our approach at SearchUnify. We see AI agent evaluation as a strategic step toward building reliable customer experiences. Before deployment, our agents undergo domain-specific testing to assess accuracy, tone, clarity, and resilience across real-world and edge scenarios.

We also apply established frameworks like Google’s Evals and RAGAs to measure retrieval and generation quality. To ensure consistent performance, we test multi-agent collaboration and monitor post-deployment outcomes through real-time dashboards—enabling teams to track impact, resolve issues, and continuously optimize.

Having nine AI Agents working 24/7 for your customers, each one focusing on a specific stage of their journey, could be transformative for businesses. This shift turns Customer Support into a strategic function, nurturing Customer relationships proactively as opposed to responding when things don’t go well.

For customers, this builds trust. Knowing that a team is looking after their potential issues before they occur strengthens their loyalty.

AI tools that track behavior, enable fast responses, and drive prompt action turn support into a competitive edge.

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This shift from reactive to proactive support, as Alejandra emphasized, is precisely what the AI agent Suite embodies. With nine dedicated AI agents working in tandem, businesses can now engage customers throughout their journey, not just when something goes wrong.

Each agent is purpose-built to solve a specific challenge, whether it’s reducing case volumes through personalized self-service, proactively resolving issues before they escalate, or extracting actionable insights from customer feedback.

The result is a connected and intelligent support ecosystem that anticipates needs, accelerates resolution and strengthens customer trust. By enabling this proactive orchestration, the AI agent Suite redefines support as a strategic advantage rather than a cost center.