What is AI observability?

A structured view of model behavior across data, prompts, outputs, and performance signals—during development and in production.

How do evaluations differ from traditional tests?

Evaluations measure end‑to‑end outcomes against business and compliance goals (not just unit tests), enabling product‑level acceptance criteria.

How did observability speed Glorium's delivery?

Shared evaluations and live dashboards gave developers immediate feedback, cut rework, and aligned stakeholders—so features moved faster to production.

What does continuous monitoring catch?

Quality regressions, data drift, prompt/model changes, and risky behaviors before they impact customers.

How are compliance teams involved without slowing dev?

They use the same evaluation results and dashboards to generate required evidence—in days instead of weeks—while teams keep building.

What measurable gains were reported?

Accelerated development cycles, lower error rates, and ~80% less recurring upkeep for one framework (~100 hours saved per year), with larger gains across multiple frameworks.

Reducing Risk While Accelerating AI Adoption

September 8, 2025

Glorium Technologies is a Digital Transformation Solutions Company working with regulated industries such as healthcare, real estate, and government. Their mission is to help enterprises adopt AI in ways that create value while meeting strict compliance and governance standards.

For Glorium, the biggest challenge in scaling AI was uncertainty. AI systems are probabilistic, and without the right controls it was difficult to assure leadership, regulators, and clients that outcomes would consistently meet expectations. Developers lacked observability into how AI models behaved, product managers had no clear way to evaluate performance, and compliance teams spent weeks documenting risks and aligning stakeholders. In one project, Glorium spent more than three months iterating on a single AI use case without reaching the level of confidence needed to move forward. This slowed down projects and limited the speed of AI adoption.

ABV was selected to address these challenges by providing an end-to-end AI validation, observability, and compliance platform with built-in safeguards. With ABV, Glorium could run structured evaluations before deployment, monitor systems continuously in production, and enforce safeguards that prevented risky behaviors from reaching customers. Evaluations became a shared tool: product managers used them to measure whether the system met business and compliance goals, while developers used the same results to identify weaknesses and fine-tune the model itself. A single set of insights was shared across developers, product managers, compliance officers, and executives. The platform was integrated quickly, allowing teams to focus on building while knowing that AI performance, safety, and compliance were being managed.

The results were clear. Development cycles accelerated, error rates decreased, and stakeholder communication became easier thanks to transparent dashboards and real-time reporting. Compliance documentation that had previously taken weeks and months could now be completed in days, and recurring compliance upkeep for a single framework was reduced by nearly 80%, saving close to 100 hours of manual work per year. For organizations managing multiple frameworks, the efficiency gains scale even higher.

ABV has taken the uncertainty out of our AI work. Our teams now have observability into what models are doing, evaluations that show where they can improve, and safeguards that give leadership the confidence to move faster.
— AI Product Manager, Glorium Technologies

With ABV, Glorium is ready to automate compliance for upcoming regulations such as the EU AI Act while building a stronger foundation of validation, observability, and safeguards. What was once a barrier to AI adoption has become an enabler of safe, responsible, and scalable innovation.

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