Responsible AI: How evaluagent builds trust, not risk

The undoubted power of Artificial Intelligence (AI) comes with real worries and responsibilities, particularly concerns over how providers use their customers’ data.

At evaluagent, we recognise these concerns and that’s why we develop to an ethical, and customer-centric standard often called ‘responsible AI’. In this article, we’ll explore what responsible AI means, why it matters, and how we make it possible for organizations like yours to embrace the hugely transformative benefits of AI whilst mitigating risk.

What is responsible AI?

At its core, Responsible AI gives the customer complete transparency over how the system works, what happens to data and why the model gives each answer. This is in contrast to ‘black box’ AI systems, where decision-making and data use is hidden from the customer.

A lot of providers, particularly in domains like CX, take a black box approach because it’s less work and gives them freedom to use customer data to train their ‘proprietary’ models. We believe customers deserve better, so have put in the extra effort to develop all our systems in a responsible way.

Taking this approach builds trust – not just with customers, but amongst your team too, helping to bring about better outcomes.

Why does responsible AI matter?

There are four key reasons why Responsible AI is critical:

1. Alignment with enterprise AI programs

    Most enterprises now have their own secure internal AI programs. These enterprises, and increasingly their counterparts in the mid-market, are becoming more aware and (rightly) protective of not relinquishing their customers’ data to murky third parties engaged in training proprietary black box models. Without a responsible approach to AI, you’re unlikely to pass the increasingly high transparency and accountability thresholds to deploy in these environments.

    2. Mitigation of risks

    Organizations are under increasing reputational pressure to account for all uses of personal data to train AI models. Concerns about privacy breaches and data misuse are driving a shift towards responsible practices that prioritize data security and integrity.

    3. Compliance with regulations

    Global regulations, such as the EU AI Act, are setting stricter guidelines for ethical AI usage. These regulations demand transparency and accountability, particularly in areas like data privacy and performance evaluation. Companies adopting a responsible approach to AI are better positioned to comply with these evolving standards.

    4. Future-proofing organizations

    As we shift to the Agentic AI paradigm, where specific tasks or workflows are handled by AI Agents working on top of a central AI architecture, we expect most businesses to build their own LLM, data store and infrastructure. Deploying your tools onto these in-house architectures is virtually impossible for black box services, which depend on their proprietary models to function. In contrast, responsible AI vendors will find it easy to replace their standard backend with a company’s own, making them future proof for the agentic age.

    How evaluagent can help you master responsible AI

    At evaluagent, we’ve built a suite of tools and practices that embody responsible AI. We offer lots of different ways for you to feel confident in how your contact center uses AI in practice.

    1. Prompt-driven innovation: Unlike competitors who rely on proprietary, ‘secret’ models, we optimise at the prompt level. Our IP resides in the questions we ask a model, rather than the specific model. This enables you to work with any AI engine you choose. This flexibility empowers businesses to maintain control over AI processes.
    2. Flexible transcription options: evaluagent integrates with leading transcription services like Deepgram. However, if you’d prefer other transcription engines, we seamlessly support those too, ensuring flexibility and customization.
    3. Efficient deep search: Our vector search technology allows users to search extensive conversation databases without relying on external AI models, making you more efficient, and your data more secure.
    4. Candidate Model Architecture: Our architecture allows clients to select the most appropriate model for each task. For example, complex problems can be routed to high-reasoning models like Anthropic’s Claude Sonnet 3.5, while simpler tasks can use more cost-effective models. This flexibility contrasts sharply with other market players who lock users into proprietary systems.
    5. Accuracy testing console: Our system lets you empirically test the accuracy of different models against real-world data. That means complete confidence in the performance of your chosen models, whether your own or one of our industry-leading providers.
    6. Private backend integration: If you’re an enterprise with proprietary Large Language Models (LLMs) of your own, you can run the entire evaluagent platform on your own infrastructure. This ensures complete data security and peace of mind, as sensitive information never leaves your environment.
    7. Reportable reasoning: Transparency is at the heart of responsible AI, and the evaluagent platform provides detailed explanations for every AI-driven decision. You can see why a model reached a particular conclusion and adjust prompts or processes if needed. This feature ensures compliance with regulations like the EU AI Act, which mandates that employees can challenge AI-based evaluations and receive actionable feedback.
    8. Data protection and privacy: We understand that there will be concerns about data being used to train proprietary LLMs, which in turn creates concerns around security and whether it’s benefiting your competitors. We never share customer data to train AI models, your data remains secure, and your privacy our priority.
    9. Model minimization: Our advanced text analytics reduce reliance on unnecessary AI usage. This not only lowers costs but also minimizes environmental impact. AI processes can consume significant resources – by optimizing AI use, we help clients save both resources and money.

    Building trust in AI, and its future

    evaluagent’s responsible AI practices offer reassurance, transparency, and flexibility without compromising the business outcomes you expect to achieve. Whether clients choose to run on our state-of-the-art infrastructure or their own secure systems, they can trust that their data remains protected, and their AI processes are accountable.

    By prioritizing responsible AI, we’re not just meeting today’s standards but setting a foundation for the future. This commitment to responsible AI does not hinder your ability to innovate. Our platform is ready to integrate seamlessly into even the most complex environments, ensuring that organizations can innovate without compromising trust, security, or compliance.

    At evaluagent, we’re committed to helping businesses like yours harness the power of AI responsibly while achieving your desired business goals. If you have any questions or would like to learn more about our approach, reach out to us for a chat.

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