For business

Christian AI evaluation for churches, schools, ministries, and organizations.

Public benchmark reports are the proof layer. The commercial product applies the same worldview-bias suite to your deployed assistant, including prompts, retrieval, policy, and release history.

What organizations buy

Ethicon AI is not mainly a public leaderboard. Organizations buy a private evaluation that scores a real deployed system in context and returns a decision-ready report.

Public scorecards are marketing. Private evaluations are the product.

What gets evaluated

  • the base model and its release version
  • the system prompt and refusal policy
  • the retrieval corpus or source bundle
  • any tools, workflow steps, or downstream decision logic
  • the release-to-release drift created by later changes

What the report answers

  1. Is the system biased against Christian moral reasoning?
  2. Does it treat Western civilizational claims with asymmetry or built-in suspicion?
  3. Does it default to moral relativism or consensus morality while presenting that as neutral?
  4. Where do those patterns show up in prompts, outputs, and scoring evidence?

Typical use cases

  • Christian education assistants
  • curriculum or publisher copilots
  • values-sensitive support and guidance systems
  • pre-launch reviews for organizations using AI in worldview-sensitive settings

Engagement structure

  1. Define the benchmark scope and risk categories.
  2. Run the suite against the configured system.
  3. Review the report with evidence-linked findings.
  4. Revise prompts, policies, or corpus inputs.
  5. Rerun the benchmark before launch or release.

Why the public site still matters

Public LLM results create trust in the method. They show that the suite can detect real differences across public systems and give prospective customers a legible proof layer before any private engagement begins.

Long-term commercial path

The first engagement can be a one-time launch review, but the stronger relationship is recurring regression monitoring. Each time the model, prompt, corpus, or policy changes, the system should be rerun against the same suite so leadership can see whether worldview drift increased or decreased.