Whether it’s helping a parent navigate a tough conversation with their child or empowering a spouse to rebuild a connection in their marriage, the quality of the model’s response and its potential to create real impact starts with the quality of its data. In AI, data is not just a building block. It’s a mirror of values, a vessel of worldview and often the very voice of the very people it seeks to serve.

What makes data “high-quality”?

As engineers, we often think in terms of precision, accuracy and performance. But in domains like faith, flourishing and human connection, the question of quality goes beyond statistical metrics. It becomes deeply contextual. What should a compassionate, biblically-grounded response look like in a Christian counseling context? How does “helpfulness” vary across different denominations, cultures and generations? When does faith-based wisdom require doctrinal fidelity and when is nuance more loving? It’s not just about building models that answer questions. It’s building ones that represent people, their language, their beliefs, their pain points and their hopes. This is where data becomes sacred. And this is where quality becomes multidimensional.

The Infinite Complexity of Faith and Flourishing Data

Designing an LLM to reason ethically, respond with theological sensitivity, or engage in emotionally intelligent ways requires more than quantity but also deep intentionality.

For example, in our work on models that serve faith leaders and families, we need to model conversation flow. This means how does a response build trust and how is empathy conveyed through tone. We need structured representations of ethical reasoning like when the model should pause, refer out or express uncertainty. We need diverse theological corpora, from sacred scripture in multiple translations to culturally-grounded sermons, devotionals and pastoral reflections. Each use case stretches the data needs. Parenting advice grounded in spiritual formation? Flourishing pathways based on holistic health and discipleship? Models that can differentiate between encouragement, correction and trauma-informed silence?

What We’re Doing at Gloo

At Gloo, unlike many other AI labs, we don’t just build automated web scrapers for data aggregation that collect everything from the internet indiscriminately or cut corners for scale. We build our corpora like you’d build a cathedral: brick by intentional brick, in community, for posterity.

We partner directly with publishers, organizations, seminaries and researchers to source and steward data. We build relationships with each of them and develop a deep understanding of their values to ensure that their content reflects values we want our models to embody and that we’re representing their content appropriately. Our pipeline includes organic data ingestion from trusted partners, synthetic data generation guided by rubrics and domain-specific prompts, and fine-tuning and evaluation using data from verified sources as test questions and custom in-house benchmarks to ensure that our LLMs meet our rigorous standards for what world-class values-aligned AI looks like.

The future of AI isn’t just about bigger models and faster inference. It’s about deeper alignment with what matters to our users. And data is the foundation of it all. In a world racing to commoditize intelligence, we’re choosing to humanize it. We believe that AI can be a servant to soul work. We believe that LLMs, when grounded in meaningful, mission-aligned data, can help people become better spouses, parents, leaders and neighbors. But only if we get the data right. Because data isn’t just input, it’s inheritance. And if we’re curating that inheritance, it is our duty to do so with reverence, wisdom and love.