What does it mean to bring digital intelligence to high-stakes environments? How to ensure that scaling LLM applications and agents also scales your understanding of outputs? In our new blog series, we focus on understanding when your AI system is improving through observability and evaluation of the AI outputs. This is the first article of our series on Building Trust When Scaling AI.
Jump forward to our Calculator: what your AI gaps are costing you
Imagine a team building LLM workflows or agents: The system is running, built as designed, but there is still uncertainty. Is this what we wanted? Does it behave as it should? Can we trust it in production? The team makes a few tweaks to the prompts, adds a button to the UI, and tests the system. However, with the non-deterministic nature of LLMs, pass or fail tests won’t do. If nobody built a way to observe and measure how well the AI system is working over time, you lose visibility into quality and scaling only multiplies this uncertainty. This leads to not knowing what works and where to start: lack of direction is the first cost of neglecting AI evaluation.
Once the system is in front of real users, the consequences of missing evaluation reach further than the team. A user who has a bad experience rarely complains but just stops using the feature or opts for another piece of software. They might not be able to put their finger on it, so they leave quietly. You won't see it in your support tickets; you will see it in weakening trust, silent churn, and in revenue that never materializes.
Evaluation in the context of LLMs means the practice of continuously evaluating the LLM outputs to ensure good quality of the AI system. Good quality includes things such as usability, security, usefulness, architecture and so on. Without systematically observing what the system is doing and measuring its quality over time, the team cannot confidently tell how good the system is, nor if a particular change is improving the system.
The teams that have moved beyond thinking about quality as an absence of crashes and understand quality as more gradual and nuanced know in which ways their system falls short. These teams are able to judge what is acceptable and where to focus their efforts – in other words, they gain direction.
Evaluation isn't your normal quality assurance, and it is not the same as testing. Quality degrades quietly, in ways that don't trigger any alarm until users are already frustrated. The old playbook doesn't work here as typically testing software has meant asking whether something works or not, passes or fails. Instead, the right questions are how it behaves and against what standard. Evaluation requires judgment about what good looks like and incorporates that into a systematic process. The point is not a single score, but rather the failure distribution.
When evaluation is in place, the team immediately sees the effect of a prompt update, a model swap, or a pipeline change in numbers. Every change has a measurable effect, and that builds confidence into decision-making. Eventually, the team stops guessing and starts knowing: they are able to stay in control and scale efficiently.
Some teams have just finished their first AI demo, others may have AI in production with some form of logging, or even with guardrails built against a few critical failure modes. Either way, without a solid and systematic evaluation in place, the direction forward is unclear. And the longer you wait, the more you have to untangle. The question is what it is costing you: in engineering time, in revenue at risk, and in trust you're losing without knowing it.
We built a calculator that shows you the number. Enter your team size, your ARR, and the gaps that apply to your system, and you'll see what your AI infrastructure gaps are costing you each year, broken down by engineering overhead and revenue at risk.
At Brillian, we guide teams in how to think about AI quality and user experience, and have built our own framework for evaluation that helps you set up processes and tools that fit your team and use case. In the next article of this series, we open up the core concepts of that framework.
The article is written by:
Samuel Rönnqvist is Lead AI Engineer at Brillian. He holds a PhD in NLP and has spent the past decade building AI systems into production and researched AI explainability, with a particular focus on reliability and building trust in AI .
Pekka Laaksonen leads growth and account development at Brillian. With a background bridging technical teams and business decision-makers, he works with companies navigating the shift to AI-driven products.
Pauliina Alanen is an AI Business Designer and Partner at Brillian, focusing on business value and user when it comes to leveraging AI.